Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies

remote sensing

Article Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies

Changyong Cao 1,* , Bin Zhang 2, Xi Shao 2, Wenhui Wang 2 , Sirish Uprety 2, Taeyoung Choi 3, Slawomir Blonski 3 , Yalong Gu 3, Yan Bai 2, Lin Lin 2 and Satya Kalluri 1

1 NOAA/NESDIS/Center for Satellite Applications and Research, College Park, MD 20740, USA; [email protected] 2 Cooperative Institute for Satellite Earth System Studies (CISESS), University of Maryland/CISESS, College Park, MD 20740, USA; [email protected] (B.Z.); [email protected] (X.S.); [email protected] (W.W.); [email protected] (S.U.); [email protected] (Y.B.); [email protected] (L.L.) 3 Global Science and Technology, College Park, MD 20740, USA; [email protected] (T.C.); [email protected] (S.B.); [email protected] (Y.G.) * Correspondence: [email protected]; Tel.: +1-301-683-3600

Abstract: Suomi NPP has been successfully operating since its launch on 28 October 2011. As one of the major payloads, along with microwave and infrared sounders (Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS)), and ozone mapping/profiling (OMPS) instruments, the Visible Infrared Imaging Radiometer Suite (VIIRS) has performed for well beyond its mission design life. Its data have been used for a variety of applications for nearly 30 environmental data products, including global imagery twice daily with 375 and 750 m resolutions, Citation: Cao, C.; Zhang, B.; Shao, X.; clouds, aerosol, cryosphere, ocean color and sea-surface temperature, a number of land products Wang, W.; Uprety, S.; Choi, T.; Blonski, (vegetation, land-cover, fire and others), and geophysical and social economic studies with nightlights. S.; Gu, Y.; Bai, Y.; Lin, L.; et al. During the early days of VIIRS operational calibration and data production, there were inconsistencies Mission-Long Recalibrated Science in both algorithms and calibration inputs, for several reasons. While these inconsistencies have less Quality Suomi NPP VIIRS impact on nowcasting and near real-time applications, they introduce challenges for time series Radiometric Dataset Using Advanced analysis due to calibration artifacts. To address this issue, we developed a comprehensive algorithm, Algorithms for Time Series Studies. and recalibrated and reprocessed the Suomi NPP VIIRS radiometric data that have been produced Remote Sens. 2021, 13, 1075. https:// doi.org/10.3390/rs13061075 since the launch. In the recalibration, we resolved inconsistencies in the processing algorithms, terrain correction, straylight correction, and anomalies in the thermal bands. To improve the stability Academic Editor: Yuyu Zhou of the reflective solar bands, we developed a Kalman filtering model to incorporate onboard solar, lunar, desert site, inter-satellite calibration, and a deep convective cloud calibration methodology. Received: 10 February 2021 We further developed and implemented the Solar Diffuser Surface Roughness Rayleigh Scattering Accepted: 9 March 2021 model to account for the sensor responsivity degradation in the near infrared bands. The recalibrated Published: 12 March 2021 dataset was validated using vicarious sites and alternative methods, and compared with independent processing from other organizations. The recalibrated radiometric dataset (namely, the level 1b or Publisher’s Note: MDPI stays neutral sensor data records) also incorporates a bias correction for the reflective solar bands, which not only with regard to jurisdictional claims in addresses known calibration biases, but also allows alternative calibrations to be applied if so desired. published maps and institutional affil- The recalibrated data have been proven to be of high quality, with much improved stability (better iations. than 0.3%) and accuracy (by up to 2%). The recalibrated radiance data are now available from 2012 to 2020 for users and will eventually be archived on the NOAA CLASS database.

Keywords: Suomi NPP VIIRS recalibration; viirs reprocessing; radiometric consistency; radiometric Copyright: © 2021 by the authors. stability; accuracy; Kalman filtering; SRRS model; WUCD; DNB reprocessing; VIIRS reprocessing system Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 1. Introduction Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ Suomi NPP has been operating successfully since its launch on 28 October 2011. 4.0/). VIIRS is one of the major payloads, along with Advanced Technology Microwave Sounder

Remote Sens. 2021, 13, 1075. https://doi.org/10.3390/rs13061075 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 1075 2 of 35

(ATMS), Cross-track Infrared Sounder (CrIS), and Ozone Mapping/Profiling (OMPS) instruments. VIIRS data have been used for a variety of applications including nearly 30 environmental data products [1] such as global imagery twice daily with 375 and 750 m resolution, cloud properties, aerosol, cryosphere, ocean color, sea-surface temperature, a number of land products (vegetation, land-cover, fire and others), as well as nighttime products for geophysical (nighttime aerosols, air glow) and social economic studies (light outage, correlation with gross domestic product). However, in the early days of VIIRS operational calibration and data production, there were inconsistencies in both algorithms and calibration inputs, for several reasons. This is further entangled with the unexpected rapid telescope mirror throughput degradation due to prelaunch contamination with tungsten oxide [2], for which several measures were implemented to mitigate its impacts on calibration, including the implementation of the RSBAutocal in the operations [3]. In addition, the Suomi NPP VIIRS reflective solar band calibration in the operations uses an outdated solar irradiance model [4], which becomes inconsistent with the later NOAA-20 calibration, which uses the Thuillier 2003 solar irradiance model [5]. Among the VIIRS radiometric performance requirements, the absolute radiometric accuracy for the reflective solar bands is specified as +/−2% (1 sigma), which is in line with that of the MODIS [6,7]. Separately, there is also the stability requirement, which states: “The VIIRS instrument response to input radiance shall not change by more than 0.1% for the emissive bands and 0.3% for the reflective bands during the time between successive acquisitions of on-orbit calibration data.” [8]. The above VIIRS calibration requirements are derived from the Key Performance Parameters (KPPs) requirements. KPPs are system attributes that represent those minimum capabilities or characteristics considered most essential to achieve an effective system. Failure to meet a KPP attribute threshold may result in a reevaluation of the program. The KPPs for VIIRS are: For latitudes greater than 60◦N in the Alaskan region, VIIRS Imagery EDRs at 0.64 µm (I1), 1.61 µm (I3), 3.74 µm (I4), 11.45 µm (I5), 8.55 µm (M14), 10.763 µm (M15), 12.03 µm (M16), and the 0.7 µm (DNB) Near-Constant Contrast (NCC) EDR [9]. However, these requirements were written from an operational product generation perspective, which relies heavily on operational forecasting and modeling for weather and ocean. In reality, the retrieval of several environmental products requires much more stringent calibration stability and absolute accuracy. For example, ocean color, aerosol, and vegetation require time series analysis with several months or years of extremely stable data in order to detect changes or anomalies. These types of studies use very sensitive algorithms with historical data and look for subtle changes in the time series or climatology. In addition, the stability and accuracy requirements for climate change studies are even more challenging [10], and this has led to the dedicated satellite missions of Climate Absolute Radiance and Refractivity Observatory (CLARREO) and TRUTH [11–13]. Therefore, calibration stability and accuracy are critical for these types of studies. From this perspective, the inconsistencies and artifacts in the operational Suomi NPP VIIRS data significantly impact their use. Thus, there is a fundamental need for recalibration. To address these issues, we have studied all relevant algorithms and methodologies in satellite calibration, developed a comprehensive algorithm and recalibrated the VIIRS radiometric data that have been recorded since launch. In the recalibration, we took into account all aspects of calibration that may affect the data quality, including inconsistencies in the processing algorithms, terrain correction, straylight correction and anomalies in the thermal bands (discussed in detail in Section2). To improve the stability of the reflective solar bands, we synthesized all calibration and validation methodologies and results, and developed a Kalman filtering model to incorporate onboard solar, lunar, desert site, inter- satellite calibration, and deep convective-cloud-based calibrations. We further developed and implemented the solar diffuser Surface Roughness Rayleigh Scattering (SRRS) model to account for the degradation in the near infrared bands, which the solar diffuser stability monitor does not cover. The recalibrated dataset was validated using vicarious sites and Remote Sens. 2021, 13, 1075 3 of 35

alternative methods, and compared with independent processing from other organizations. The recalibrated sensor data records or level 1b radiance data incorporate a bias correction, which addresses all known biases, and also allows alternative calibrations to be derived if so desired. The recalibrated data have been proven to be very stable, and the algorithm is novel and robust. The recalibrated radiance data, which were processed with a high- performance computing system (AppendixA), are now available from 2012 to 2020, on request, for users, and will eventually be archived in the NOAA Comprehensive Large Array-data Stewardship System (CLASS) database [14].

2. Past VIIRS Recalibration Efforts Several studies have attempted to recalibrate the Suomi NPP VIIRS sensor data records [3,15–18]). During the first few years after the launch of the Suomi NPP satellite, radiometric and geolocation calibration of the VIIRS SDR operational products from NOAA Interface Data Processing Segment (IDPS) was improved multiple times [15–17]. The main goal of the early reprocessing efforts was to apply the improved operational calibration to the VIIRS data acquired since the Suomi NPP launch. For the reflective solar bands (RSB), as well as for the day/night band (DNB), the primary issue was the correction of the VIIRS telescope throughput degradation detected shortly after launch. That continuing degradation required the implementation of an automated solar calibration procedure, which was applied in the Sensor Data Record (SDR) production by the end of 2015 [3], and the initial reprocessing allowed for the validation of the automated approach. For the thermal emissive bands (TEB), the initial emphasis was on consistency between the radiance and brightness temperature products, followed by improvements in the calibration during the onboard blackbody warm-up/cool-down tests that were then conducted for 2–3 days every 3 months [19,20]. For the VIIRS Day/Night Band (DNB), calibration improvements included corrections of the spectral response changes due to the telescope degradation, consistent derivation and application of the dark offset and gain ratio parameters, as well as the stray light corrections that have been affecting DNB near the day/night terminator since the Suomi NPP launch. Geolocation improvements involved the initial removal of biases after launch and adjustments made after scan-controller-configuration changes, and a star tracker realignment in 2012 and 2013 [21]. These early reprocessing results formed a baseline, on which further enhancements of the VIIRS SDR radiometric and geolocation products were built. Extensive studies were carried out on further improving the VIIRS reflective solar band calibration. In 2015 [22], it was recognized that the operationally produced VIIRS SDR solely relied on onboard solar diffuser calibration, and did not incorporate lunar calibration, which revealed residual degradation in the operational products. There had been user- oriented requests of the entire mission lifetime recalibration to improve initial calibration changes, mid-mission anomalies, and calibration updates. On top of these improvements, the on-orbit calibration coefficients in the Reflective Solar Bands (RSBs), called the F-factors, calculated from the Solar Diffuser (SD) observations, were gradually deviating from the lunar F-factors that were calculated from the monthly scheduled lunar observations [22–25]. These SD and lunar F-factor differences were observed by other teams and the differences in the SD and lunar calibrations were well documented [22,23,25]. Table1 summarizes the calibration algorithms which are used in the production of VIIRS SDR. The NOAA Ocean Color (OC) team initially applied corrections from SD to lunar trends, recognizing that the lunar provided more accurate sensor degradation than SD [26]. For the short wavelength bands (M1–M4), the OC team’s operational calibration coefficients were fitted to the long- term trends of the lunar F-factors, and were named “hybrid calibration coefficients”; this was validated against the long-term water leaving radiance trends from the Marine Optical Buoy (MOBY) data [26]. Remote Sens. 2021, 13, 1075 4 of 35

Table 1. Calibration Algorithms used in the Production of Visible Infrared Imaging Radiometry Suite (VIIRS) Sensor Data Records.

Versions Status Solar Comments Time Period Irradiance Model Used Reference Operationally Not Modtran consistent. Early data Operational October 2011–present produced and 4.3 [4] archived on CLASS have many artifacts Calibration coefﬁcients January 2012–May 2020 Modtran RSBautocal regenerated but data 4.3 [15] not reprocessed Incorporated in Version 1 January 2012–May 2020 Modtran (removed Oscillation) version 2 bias 4.3 [27] correction Generated by NOAA Modtran Hybrid (OC F-factor) January 2012–March 2017 OC group for 4.3 [22] solar bands Calibration V1.9 (using coefﬁcients Modtran MODTRAN November 2011–March 2018 regenerated but data [27] solar irradiance) 4.3 not reprocessed Reprocessed and Kalman V2.0 January 2012–May 2020 Thuillier 2003 ready for distribution

Separately, the NASA VIIRS Calibration Support Team (VCST) developed an independent on-orbit VIIRS calibration using a different SD degradation at the Rotating Telescope Assembly (RTA) angle to the solar diffuser (SD) surface (H-RTA) [28,29]. The study assumed that the long-term SD and F-factor differences were caused by the solar diffuser stability monitor (SDSM) and the RTA SD view vector differences in the SD surface normal and the solar vectors. In addition, they developed a simple phenomenological model to correct the annual oscillations and the differences between the SD and lunar F-factors. The number of corrections was slightly different among all the teams of NASA VCST, NOAA VIIRS team, and NOAA OC team, and the differences were summarized in a study [18]. Since the OC team’s hybrid calibration coefficients were optimized for their use (low reflectance range for ocean-color applications), they may not produce the best results for other Environment Data Record (EDR) teams, where the reflectance is significantly higher. In this situation, the NOAA VIIRS team developed a baseline VIIRS calibration, which is described in the official VIIRS Algorithm Theoretical Basis Document (ATBD), and used version 1 products for reprocessing [30]. The official implementation of the ATBD-based VIIRS calibration was implemented to RSBAutoCal as a baseline RSB-reprocessing calibration version 1 [8,15]. For VIIRS on-orbit automatic radiometric calibration, the RSBAutoCal is currently included in the Algorithm Development Library (ADL) package, and it is also a part of the NOAA’s operational Interface Data Processing Segment (IDPS) [31]. The version 1 S-NPP recalibrated datasets were produced by an offline software package called ADL version 4.2 with Mx 8.11, which includes accumulative updates over five years, such as early mission calibration updates, H-factor changes in 2014, c-coefficient update [32], and solar vector reference change [33].

3. Current Recalibration Methodology, Algorithms, and Improvements This section may be divided by subheadings. It should provide a concise and pre- cise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.

3.1. Improvements in Absolute Radiometric Accuracy While there may be many different deﬁnitions of absolute radiometric accuracy, in this paper, we deﬁne it as the uncertainty of the VIIRS radiometric measurement relative to the International Systems of Unit (SI) traceable absolute radiometric source, which is typically characterized during prelaunch calibration against a national laboratory Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 34

3.1. Improvements in Absolute Radiometric Accuracy: While there may be many different definitions of absolute radiometric accuracy, in Remote Sens. 2021, 13, 1075 this paper, we define it as the uncertainty of the VIIRS radiometric measurement relative5 of 35 to the International Systems of Unit (SI) traceable absolute radiometric source, which is typically characterized during prelaunch calibration against a national laboratory stand- standardard source source and validated and validated on-orbit. on-orbit. Traceability Traceability is the isproperty the property of a measurement of a measurement result, result,whereby whereby the result the can result be related can be to related a reference to a referencethrough a through documented a documented unbroken chain unbro- of kencalibrations, chain of calibrations, each contributing each contributing to the to the measurement measurement uncertainty (BIPM, (BIPM, https://www.bipm.org/en/bipm-services/calibrations/traceability.htmlhttps://www.bipm.org/en/bipm-services/calibrations/traceability.html, access, accesseded on 3131 AugustAugust 2020). 2020). Absolute Absolute radiometric radiometric accuracy accuracy is oneis one of theof the key key performance performance parameters parameters for allfor satellite all satellite radiometers. radiometers. Compared Compared to the to thermal the thermal emissive emissive bands bands and DNB, and theDNB, RSB the is theRSB mostis the complex most complex and challenging and challenging in terms in of terms improving of improving the radiometric the radiometric accuracy accuracy and stability. and Instability. this section, In this we section, focus we on thefocus RSB on improvementsthe RSB improvements in the latest in the version latest ofversion reprocessing, of repro- whichcessing, involves which involves a major changea major inchange the calibration in the calibration algorithm algorithm and methodology. and methodology. We then We discussthen discuss the incremental the incremental improvements improvements in the in TEB the and TEB DNB. and DNB. ThreeThree major major issues issues related related to to absolute absolute accuracy accuracy were were revealed revealed in in previous previous studies: studies: (1) (1) TheThe solar solar irradiance irradiance model model used used in thein the operational operational processing processing and earlierand earlier reprocessing reprocessing was outdated,was outdated as shown, as shown in Figure in Figure1[ 4]; 1 (2) [4]; Based (2) Based on validation on validation and userand feedback,user feedback, there there are radiometricare radiometric biases biases in the in the M5 M5 and and M7 M7 (I2 (I2 as well,as well, but but not not I1) I1) bands, bands, which which are are about about 2% 2% higherhigher thanthan whatwhat they they should should be be based based on on comparisons comparisons with with MODIS MODIS and and independent independent observations;observations; (3)(3) The The latest latest comparison comparison with with NOAA-20 NOAA-20 shows shows that that Suomi Suomi NPP NPP VIIRS VIIRS measuredmeasured reﬂectancereflectance are higher than than those those of of NOAA NOAA-20-20 bands bands by by2% 2%or more or more for all for solar all solarbands. bands.

FigureFigure 1. 1.Radiometric Radiometric biases biases due due to to the the use use of of different different solar solar irradiance irradiance models models (MODTRAN 4.0 vs. Thuillier(MODTRAN 2003) 4.0 vary vs. up Thuillier to 3%, depending2003) vary onup theto 3% channel., depending on the channel.

AlthoughAlthough thethe biasbias resultingresulting fromfrom thethe use use of of different different solar solar irradiance irradiance models models only only affectsaffects the the radiance radiance measurements, measurements, not not reﬂectance, reflectance if computed, if computed correctly, correctly, it causes it causes problems prob- inlems radiance-based in radiance-based inter-sensor inter-sensor comparisons. comparisons. In the In recalibration, the recalibration, this discrepancy this discrepancy was resolvedwas resolved by consistently by consistently using using the Thuillierthe Thuillier 2003 2003 solar solar irradiance irradiance model. model. As As a a result, result, thethe radiometric radiometric consistency consistency withwith other other instruments, instruments,such such as as NOAA-20 NOAA-20 VIIRS, VIIRS, has has been been improvedimproved by by up up to to 3% 3% for for some some channels channels in in the the recalibrated recalibrated SDR SDR data. data. The The M5 M5 and and M7 M7 radiometricradiometric biases biases were were discovered discovered by by multiple multiple users users (both (both NOAA NOAA and and NASA NASA Cloud Cloud and and AerosolAerosol teams) teams) and and independently independently veriﬁed verified by by the the VIIRS VIIRS SDR SDR team team at desert at desert sites sites [4,34 [4,3–364].– In36 the]. In recalibration, the recalibration, this issue this issue has been has been addressed, addressed as discussed, as discussed in Section in Section4. 4.

3.2. Improvements in Long-Term Radiometric Stability As is discussed in Section1, the radiometric stability requirement of 0.3% is much more stringent than the absolute calibration accuracy (+/−2%). These two measures go hand in hand. Assuming that absolute calibration accuracy is established at a few checkpoints (such as prelaunch SI traceable calibration), a stable calibration is essential to ensure the calibration accuracy in the time series of the measurements on-orbit. Remote Sens. 2021, 13, 1075 6 of 35

Historically, the stability of the RSB relied on the onboard solar diffuser, and was independently veriﬁed with vicarious calibration. However, in recent years, it was found that residual degradation, and imprecise characterization of the onboard calibration system, can lead to drifts in the calibrated radiances. As a result, a number of methods have been used to ensure the stability of the calibration. This includes the dedicated monthly lunar calibration for VIIRS, and vicarious calibration using deep convective clouds, and desert sites, as well as cross-calibration with other stable satellite measurements at the simultaneous nadir overpasses. Table2 summarizes the various calibration methods, as well as their pros and cons for ensuring stable and accurate calibrated radiances.

Table 2. Reﬂective Solar Band Calibration Methodologies.

Calibration Method Advantages Limitations Frequent calibration (up to once per Diffuser itself degrades over time; Onboard Solar Diffuser w/Solar diffuser orbit); not affected by atmosphere; residual degradation may not be stability monitor (SDSM) uniform and stable; absolute accuracy accounted for even with the SDSM; NIR based on uncertainty budget analysis bands (M8-M11, I3) not covered by SDSM Lunar reflectance is extremely stable; Only 9 out of 12 months lunar cal is monthly lunar calibration maneuver at achievable (summer gap due to large Lunar calibration same lunar phase angle can reduce spacecraft roll angle); each month only uncertainties in stability down to has one datapoint; requires longer time sub-percent level in time series period (at least one year) to detect trend. Atmospheric effect still exists; site bidirectional reflectance distribution Desert sites reflectance are considered function (BRDF) effect introduces Desert/vicarious site calibration pseudo-invariant; more accessible for all seasonal uncertainties; site may not be satellites; ground validation is possible stable; cloud contamination reduces the number of useable samples. Not all sites are suitable. Limited to the polar regions for polar Compares calibration with those from -orbiting satellites; extension to low SNO inter-satellite other satellites with low uncertainties latitudes compromises view angle and calibration using coincident observations time widow; absolute values not established. Not affected by atmosphere due to height; Clouds have no fixed location or shape; bright and stable, spectrally relatively flat relies on large sample statistics to reduce Deep convective clouds in the visible spectrum; more uncertainties; absolute reflectance accessible globally. affected by BRDF

3.3. A Comprehensive Approach for Improved Accuracy and Long-Term Stability: The Kalman Filter Approach It is recognized that the VIIRS onboard calibration system (solar diffuser and solar diffuser stability monitor) alone is not sufﬁcient to ensure calibration stability, due to residual degradation effects. Therefore, lunar-based calibration has been used to correct the residual degradation of VIIRS RSBs which was not captured by the onboard calibration system due to the angular-dependent degradation of solar diffuser BRDF [22,26,28]. However, lunar calibration only has a few datapoints per year, and there is a gap in the summer months when no lunar calibration is available. As a result, in addition to lunar calibration, a number of other methods have been developed and matured over the years for satellite radiometer calibration/validation, including the Deep Convective Clouds (DCC), pseudo-invariant calibration targets (PICS) such as desert sites, and inter-satellite calibration at simultaneous nadir overpass (SNO) and extended SNO with other low-Earth-orbit (LEO) sensors. All of these provide an independent evaluation of the radiometric stability and accuracy of VIIRS RSBs, and each has its own advantages and limitations. The challenge is to reconcile these independent results and provide a comprehensive estimate for the VIIRS sensor in terms of stability, degradation, and absolute accuracy, which are essential for time series analysis. Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 34

months when no lunar calibration is available. As a result, in addition to lunar calibration, a number of other methods have been developed and matured over the years for satellite radiometer calibration/validation, including the Deep Convective Clouds (DCC), pseudo- invariant calibration targets (PICS) such as desert sites, and inter-satellite calibration at simultaneous nadir overpass (SNO) and extended SNO with other low-Earth-orbit (LEO) sensors. All of these provide an independent evaluation of the radiometric stability and accuracy of VIIRS RSBs, and each has its own advantages and limitations. The challenge is to reconcile these independent results and provide a comprehensive estimate for the

Remote Sens. 2021, 13, 1075 VIIRS sensor in terms of stability, degradation, and absolute accuracy, which are7 of 35essential for time series analysis. The approach used in this study for synthesizing the various calibration approaches and results is the Kalman filter method [37], which has numerous applications, such as The approach used in this study for synthesizing the various calibration approaches the navigation of vehicles, aircrafts and satellites, motion control, time series analysis in and results is the Kalman filter method [37], which has numerous applications, such as signalthe navigation processing of vehicles,and econometrics, aircrafts and and satellites, data assimilation motion control, [38 time]. Multi series-sensor analysis data in fusion basedsignal on processing Kalman andfiltering econometrics, has been and extensively data assimilation studied [38 and]. Multi-sensor successfully data applied fusion in vari- ousbased areas on Kalman[39–41] filteringto reduce has uncertainty been extensively through studied information and successfully fusion. applied In this in paper, various the Kal- manareas filtering [39–41] totechnique reduce uncertainty is applied through to combine information the sensor fusion. response In this paper, variations the Kalman monitored withfiltering independent technique isapproaches applied to combineto form thean optimal sensor response determination variations of monitored the true degradation with inindependent the VIIRS approachesinstrument to response. form an optimal Figure determination 2 shows the offramework the true degradation of the Kalman in the Filter- basedVIIRS Calibration instrument response.Data Fusion Figure (KFCDF)2 shows system the framework we developed of the for Kalman VIIRS Filter-based RSBs, with wave- Calibration Data Fusion (KFCDF) system we developed for VIIRS RSBs, with wavelength < length < 1 µm, to support optimal calibration correction factor determination from multi- 1 µm, to support optimal calibration correction factor determination from multiple sources pleof calibrationsources of data calibration input. data input.

Figure 2. Figure 2. FrameworkFramework ofof Kalman-Filter-basedKalman-Filter-based Calibration Calibration Data Data Fusion Fusion (KFCDF) (KFCDF) system system for VIIRS for VIIRS µ (R(Reﬂectiveeflective Solar BandsBands (RSBs) (RSBs with) with wavelength wavelength < 1

location of the Sun vary for each solar calibration, both the transmission screen and the SD reﬂectance need to be characterized as a function of solar incidence angles, i.e., bidirectional reﬂectance distribution function (BRDF). The BRDF characterization of the SD system was performed during the pre-launch testing and updated with the on-orbit yaw maneuver Remote Sens. 2021, 13, 1075 8 of 35

measurements. There were remnant oscillations in long-term VIIRS solar-F factors due to imperfect characterization of SDSM screen transmittance and SD BRDF with yaw maneuver measurements alone. An earlier study developed a scheme to minimize the Solar-F factor Remote Sens. 2021, 13, x FOR PEER REVIEW oscillations by reanalyzing the yaw maneuver data to cover more comprehensive9 of 34 solar incidence angular geometry [27]. The current paper uses the time series of daily solar-F factor for VIIRS RSBs, derived in [27] as inputs to KFCDF. Figure3a shows an example of solar-F factor time series for VIIRS M2.

FigureFigure 3. Five 3. Five types types of time of series time of VIIRSseries sensor of VIIRS radiometric sensor performance radiometric monitoring performance measurements monitoring analyzed measure- with trends illustratedments analyzed using VIIRS with M2 astrend an example.s illustrated (a) The using solar-F VIIRS and lunar-F M2 as factor an example. time series. (a) ( bThe,d) Time solar series-F and of top-of-the-lunar-F atmospherefactor time (TOA) series. reﬂectance (b,d) overTime Deep series Convective of top-of Clouds-the-atmosphere (DCC) and Libya, (TOA respectively.) reflectance (c) The over percent Deep difference Convec- in VIIRStive M2 Clouds w.r.t. MODIS (DCC) from and SNOx; Libya, blue respectively. curve in each (c panel) The shows percent the trenddifference in stability-monitoring in VIIRS M2 w.r.t. measurement MODIS data after the preprocessing. from SNOx; blue curve in each panel shows the trend in stability-monitoring measurement data after the preprocessing. Table 3. Characteristics of Calibration Stability Monitoring Methods for VIIRS RSB.

Solar Lunar The moon-based calibration coefficients (lunarDCC F-factors) are calculated SNOx by taking Desert the Calibration Calibration ratio between the observed and modeled lunar irradiance. In [18], the Global Space-based At most At most Monthly with InterData -Calibration System (GSICS) Implementation of ROLO (GIRO)8 days model and is used16-days to de- and Every orbit 3–4 months gap Monthly Frequencyrive the lunar-F factors. The current paper ingests the time seriesaffected of the by cloud lunar-Faffected factor by de- cloud each year rived in [18] into the KFCDF processing system. Figure 3a showscontamination. an example ofcontamination. lunar-F Startingfactor Date time series 8 November for VIIRS 2011 M2 2 band. April 2012 It can be 15 seen February that 2012 the lunar 8 January F-factor 2012 deviates 18 January from 2012 the solar F-factor by as much as ~1% for the VIIRS M2 band due to overestimation of the SD BRDF degradation inThe the SD solar reﬂectance F-factor. value The decreasesre are overalso timeapparent and its annual degradation oscillations is monitored in by SDSM. However, the SDSM monitors the SD reﬂectance degradation at a different view the lunar-F factor timegeometry series from, which the VIIRS may Rotating arise from Telescope the Assembly unaccounted (RTA) viewlunar of SD.libration It was suggestedand other uncertaintiesthat in the SDlunar BRDF irradiance degradation model. has angular Other dependency uncertainties [15,23– 28in], lunar especially F-factor for M1-M4 calculations may come from the derivation of lunar irradiance from multiple scans of the moon by 16 (M band) or 32 (I band) detectors of VIIRS during each scan, such as oversampling factor determination and background offset subtraction. In addition, the 3–4- month gap in moon observations each year can also impact the long-term trending of lunar F-factors. (c) Deep Convective Cloud (DCC) Data DCCs are extremely cold, near Lambertian and radiometrically stable targets that have been widely used for the on-orbit calibration stability monitoring of satellite radiometers in the reflective solar spectrum. The DCC technique has been demonstrated in our previous studies [45,46] for VIIRS operational calibration monitoring and reprocessing improvements’ evaluation. In this study, monthly DCC time series were generated for the reprocessed S-NPP RSB SDRs using a similar method. DCC pixels are identified using M15 brightness temperatures. The anisotropic effects in the VNIR bands’ DCC data were corrected using the HU2004 angular distribution model [47]. Moreover, uniformity

Remote Sens. 2021, 13, 1075 9 of 35

of SNPP VIIRS, whose SD reflectance degraded faster [42–44]. The solar-F factor did not account for the post-launch angular-dependent degradation in solar diffuser BRDF for these bands. In addition, the solar-F factor carries the uncertainties of the solar irradiance model, SDSM and SD screen transmittance and SD BRDF determined from pre-launch and post-launch measurements. Therefore, solar-F factor itself cannot adequately monitor the radiometric performance of VIIRS RSBs, especially M1–M4. (b) Lunar-F factor The photometric stability of the lunar surface and its smooth reflectance spectrum make the moon an ideal target for calibrating spaceborne hyperspectral and multispectral RSB imagers. The observation of the moon by SNPP VIIRS is not subject to the atmospheric absorption and scattering effects. Therefore, moon-based radiometric calibration has been used as an independent means of monitoring and validating VIIRS RSB performance. During lunar calibration for VIIRS, spacecraft roll maneuvers are usually performed to acquire moon-view data in the Space View port. Moon data collections were performed monthly at nearly the same lunar phase angle of ~51 degree, with waxing lunar phase, except for 3–4 summer months each year [18,23]. The moon-based calibration coefficients (lunar F-factors) are calculated by taking the ratio between the observed and modeled lunar irradiance. In [18], the Global Space-based Inter-Calibration System (GSICS) Implementation of ROLO (GIRO) model is used to derive the lunar-F factors. The current paper ingests the time series of the lunar-F factor derived in [18] into the KFCDF processing system. Figure3a shows an example of lunar-F factor time series for VIIRS M2 band. It can be seen that the lunar F-factor deviates from the solar F-factor by as much as ~1% for the VIIRS M2 band due to overestimation of the SD BRDF degradation in the solar F-factor. There are also apparent annual oscillations in the lunar-F factor time series, which may arise from the unaccounted lunar libration and other uncertainties in the lunar irradiance model. Other uncertainties in lunar F-factor calculations may come from the derivation of lunar irradiance from multiple scans of the moon by 16 (M band) or 32 (I band) detectors of VIIRS during each scan, such as oversampling factor determination and background offset subtraction. In addition, the 3–4-month gap in moon observations each year can also impact the long-term trending of lunar F-factors. (c) Deep Convective Cloud (DCC) Data DCCs are extremely cold, near Lambertian and radiometrically stable targets that have been widely used for the on-orbit calibration stability monitoring of satellite radiometers in the reflective solar spectrum. The DCC technique has been demonstrated in our previous studies [45,46] for VIIRS operational calibration monitoring and reprocessing improvements’ evaluation. In this study, monthly DCC time series were generated for the reprocessed S-NPP RSB SDRs using a similar method. DCC pixels are identified using M15 brightness temperatures. The anisotropic effects in the VNIR bands’ DCC data were corrected using the HU2004 angular distribution model [47]. Moreover, uniformity thresholds for RSB reflectance and M15 BT were also applied. Details of the DCC technique used in this study are available in [45,46]. In this section, the time series of monthly DCC mode data derived from visible and near infrared (VNIR) SDRs were used to characterize residual degradation after the solar calibration. Seasonal cycles in the DCC time series were removed using a method developed in [48]. Then, monthly DCC time series data were ingested as one type of calibration data input to the KFCDF model. Figure3b shows an example of such DCC time series derived from VIIRS M2 radiance data. (d) SNOx with MODIS over Sonoran Desert When two or more satellites orbit the earth at different altitudes, simultaneous nadir overpasses (SNOs) periodically occur [49]. The SNO technique has been used to independently quantify the VIIRS radiometric performance w.r.t. MODIS instruments at their orbital intersection, with a small time-difference between the instruments’ observation [34,35]. Remote Sens. 2021, 13, 1075 10 of 35

The comparison of simultaneous measurements at their orbital intersection allows nearly identical viewing conditions, making this approach extremely suitable in inter-calibration, with reduced uncertainties associated with atmospheric absorption variability and BRDF. While SNOs occur mostly in high-latitude polar regions with limited scene types, there are extended SNO events between SNPP VIIRS and Aqua MODIS at low latitudes, every 2– 3 days, but with larger time differences, of more than 8 min. SNOx in low latitudes enables the inter-comparison of sensors over a wide dynamic range, such as over ocean surface, desert target, and green vegetation. A previous study has demonstrated the use of SNOx to study the on-orbit radiometric performance of VIIRS onboard SNPP [35]. VIIRS top-of-the- atmosphere (TOA) reflectance measurements are compared with matching MODIS bands. Aqua MODIS collection 6.1 data are used in this study. Figure3c shows example time series of TOA reflectance-based SNPP VIIRS bias (%) relative to Aqua MODIS, derived using the SNOx method for the VIIRS M2 band. The main uncertainties of the SNOx method are due to cloud movement, residual cloud contamination and cloud shadow, BRDF due to view geometry differences, atmospheric absorption variability, and spectral differences between sensors. In addition, the SNOx method is subject to uncertainty in the radiometric accuracy and stability of MODIS measurements. (e) Vicarious monitoring over Libyan desert Pseudo-invariant calibration sites (PICS) have been widely used for characterizing the radiometric performance (temporal stability and accuracy) of satellite sensors for RSBs. PICS sites are homogenous and very stable in the long term. It is desirable to compare instruments over well-characterized homogenous calibration targets to reduce uncertainties resulting from registration errors. In the past, efforts have been focused on analyzing the radiometric performance of VIIRS using PICS such as Libya-4 and Dome-C [34]. This study analyzes the stability of SNPP VIIRS RSBs by using nadir observations of the Libya-4 desert. The Libya-4 desert is a Committee on Earth Observation Satellites (CEOS)-endorsed calibration site. Figure3d shows an example time series of TOA reflectance over the Libya-4 desert for the VIIRS M2 band without BRDF and atmosphere correction. SNPP VIIRS has a repeating ground track cycle of 16 days. Therefore, the TOA reflectance dataset over Libya-4 is, at most, one datapoint every 16 days, and this can be sparser due to cloud contamination. The inherent impacts of sparse dataset, BRDF dependence and atmospheric absorption variability on the measurements over PICS make quantifying the radiometric stability of VIIRS more challenging than when using other methods. This can be seen from the larger fluctuations in the TOA reflectance series shown in Figure3d in comparison with other calibration-stability-monitoring data. Table2 summarizes the key features of these five calibration-stability-monitoring methods in terms of their data sample frequency, advantages and uncertainties. The characteristic sampling intervals for these time series of stability monitoring data vary irregularly, from per orbit to monthly, depending on the particular monitoring scheme. To reconcile the large differences in data sampling frequency, the calibration stability monitoring data are smoothed to remove seasonal variations and reduce uncertainties, and then are interpolated to the same daily time resolution. The resulting trending data after preprocessing are recorded as FSolar, FLunar, TDCC, TSNOx, and TDesert for the five stability monitoring schemes, respectively. Figure4 shows an example of stability trending derived for VIIRS M2 using these five schemes. While the solar-F factor is almost flat, the lunar-F factor shows an upward trend, which indicates that the M2 detector response degrades. DCC, SNOx and Libya desert monitoring all show downward trends. These trends are derived from the SDR data that accounted for the solar-F factor derived from the onboard solar diffuser calibration. Using stable vicarious sites, such as DCC and the Libya desert or MODIS sensor, as a reference, the downward trend in these three stability monitoring methods reveals the degradation of the M2 detector response that the solar calibration did not capture. Therefore, lunar calibration, DCC, SNOx and Libya desert monitoring all consistently show the degradation of VIIRS M2, which was unaccounted for in the solar calibration, possibly due to the Remote Sens. 2021, 13, 1075 11 of 35

angular-dependent degradation of SD BRDF. To quantitatively reconcile these stability monitoring time series and establish a true estimate of calibration F factors for VIIRS RSBs, data fusion of multiple stability monitoring data is performed with Kalman ﬁltering, i.e., KFCDF. Since the desert stability monitoring time series have larger uncertainties and Remote Sens. 2021, 13, x FOR PEER REVIEWsparsity due to uncorrected desert BRDF effects and atmospheric absorption, as shown12 of in 34

Figure3, the KFCDF modeling screened out TDesert and focused on the data fusion of FSolar, FLunar, TDCC, and TSNOx time series data.

FigureFigure 4. 4.Examples Example ofs of Kalman-ﬁltering-based Kalman-filtering-based assimilation assimilation of timeof time series series of solar-F, of solar lunar-F,-F, lunar DCC-F-F, DCC and- simultaneousF and simultaneous nadir overpassnadir overpass (SNO)x-F (SNO factors)x-F forfactors M1-M4 for M1 of SNPP-M4 of VIIRS. SNPP InVIIRS. each In panel, each thepanel, black the curve black shows curve theshows optimal the optimal F factor F time factor series time estimatedseries estimated with the with Kalman-ﬁlter-based the Kalman-filter calibration-based calibration data fusion data (KFCDF) fusion (KFCDF model) for model the correspondingfor the corresponding band. band.

3.3.2.3.3.3. NormalizationKalman Filter- ofBased the FVIIRS Factor Calibration Data Fusion WhileThe kernel solar-F of andthe KFCDF lunar-F processing factors provide system direct for VIIRS characterization is the customized of the sensor Kalman gain fil- change,ter, used the to DCC-estimate and the SNOx-based time series of VIIRS the optimal stability F factor monitoring for trending data are VIIRS derived sensor by gains. pro- cessingIt combines the SDRs independent generated VIIRS when calibration the time-varying stability solar-F measurements factor is applied.such that Therefore, the resulting to characterizeF factor time the series stability has ofless VIIRS uncertainty RSB bands than with wouldTDCC ,be and possibleTSNOx ,when equivalent these calibrationcalibration Fdatafactors are used need individually. to be derived This from scheme these focuses stability on trending calibration time uncertainty series data. reduction The trend. Fig- observedure 5 shows by DCCthe steps measurement involved in reveals the KFCDF the detector scheme. gain variation, in addition to those accountedAs illustrated for in the in solar-F Figure factor. 5, the Therefore,Kalman-filtering the DCC-F-based factor calibration can be deriveddata fusion as process for VIIRS can be divided into two phases, the prediction phase and update phase, as fol- FSolar(t) lows. FDCC(t) = , for t ≥ t0,DCC, (1) TDCC(t)/(TDCC(t0,DCC)) Predict wherePredictedt0,DCC is VIIRS the starting sensor timestate ofestimate DCC data. (a priori): Similarly, the SNOx-based F factor, i.e., SNOx-F, can be derived from the time series of SNOx trending data as 푥푘|푘−1 = 푥푘−1|푘−1 (3) Predicted sensor covariance estimateFSolar (a(t )priori): FSNOx(t) = , for t ≥ t0,SNOx, (2) (1 + TSNOx(t)/100)/(1 + TSNOx(t0,SNOx)/100) 푃푘|푘−1 = 푃푘−1|푘−1 + 푄푘 (4) where t ≥ t is the starting time of SNOx data. Note that T (t) trending data are Update0,SNOx SNOx recorded as the bias in percent difference between MODIS and VIIRS. Innovation covariance 푇 푆푘 = 퐻푘푃푘|푘−1퐻푘 + 푅푘 (5) Optimal Kalman Gain

푇 −1 퐾푘 = 푃푘퐻푘 푆푘 (6) Updated VIIRS sensor state estimate (a posteriori)

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Remote Sens. 2021, 13, 1075 12 of 35 푥푘|푘 = 푥푘|푘−1 + 퐾푘(푦̃푘 − 퐻푘푥푘|푘−1) (7) Updated covariance estimate (a posteriori)

Figure4 shows the time series푃 of푘|푘 solar-F,= (퐼 − lunar-F,퐾푘퐻푘)푃푘 DCC-F|푘−1 and SNOx-F factors for M1-(8) M4 of SNPP VIIRS. Lunar-F, DCC-F and SNOx-F all show deviations from the solar-F factor In the above formulations for KFCDF, 푥푘 is the estimated true VIIRS sensor F factor for these four bands. For VIIRS M2-M4, all three F factors are consistently trending to higher state and 푃푘 is the covariance matrix of the state or error estimate. 푦̃푘 = values than solar-F, which indicates that the additional detector degradation over time is (퐹푆표푙푎푟, 퐹퐿푢푛푎푟, 퐹퐷퐶퐶, 퐹푆푁푂)푘 is a heterogeneous VIIRS sensor performance measurement unaccounted for by solar-F for these bands. These four F factors, derived from stability vector in terms of equivalent F factors from four types of measurement. In Equation (7), monitoring data, provide an independent evaluation of the sensor response variation over 푦̃푘 − 퐻푘푥푘|푘−1 is also called the innovation residual of measurements. 퐻푘 is the observa- time, and are fed into the kernel of KFCDF for true calibration F factor estimation. tion model and is set as unity after the 푦̃푘 and populated with equivalent F factors from 3.3.3.diverse Kalman measurements. Filter-Based 푄푘 VIIRS and 푅 Calibration푘 are the covariance Data Fusions of the process noise and the observation noise, respectively, and are estimated with time-lagged auto-covariance. At each The kernel of the KFCDF processing system for VIIRS is the customized Kalman ﬁlter, instant, in the predict phase, the priori estimated VIIRS F factor and covariance are used used to estimate the time series of the optimal F factor for trending VIIRS sensor gains. It to predict VIIRS sensor state with Equations (3) and (4). Then, the VIIRS F factors derived combines independent VIIRS calibration stability measurements such that the resulting F from heterogeneous measurement are used to calculate the innovation residual with 푦̃ − factor time series has less uncertainty than would be possible when these calibration data푘 퐻 푥 , innovation covariance and Kalman gain using Equations (5) and (6). The esti- are푘 used푘|푘−1 individually. This scheme focuses on calibration uncertainty reduction. Figure5 mated true F factor 푥 and covariance 푃 are updated with Equations (7) and (8), shows the steps involved푘|푘 in the KFCDF scheme.푘|푘 which serve as the priori for the next KFCDF update.

Figure 5. Schematics of KFCDF process for SNPP VIIRS data recalibration. Figure 5. Schematics of KFCDF process for SNPP VIIRS data recalibration.

AsThe illustrated adaptive nature in Figure of 5Kalman, the Kalman-ﬁltering-based filtering enables the fusion calibration of multiple data fusioncalibration pro- cessdata for to estimate VIIRS can the be true divided VIIRS intosensor two calibration phases, the F factor prediction time series phase, which and updateare shown phase, as asblack follows. curves in Figure 4 for VIIRS M1-M4. At each instant, the true F factor estimation providesPredict an optimal determination of gain values in calibration. Most often, the F factor fromPredicted KFCDF is VIIRS aligned sensor with state lunar estimate-F factor (a, priori):as shown in Figure 4, which validates the importance and robustness of lunar calibration for VIIRS RSBs. For VIIRS M1 and M3, the

F factors from KFCDF start to deviatex kfrom|k−1 =lunarxk−-1F|k after−1 2016, when the DCC-F and SNOx(3)- F factors became more consistent. The KFCDF scheme has been applied to recalibrate the gainPredicted coefficients sensor of VIIRS covariance M1-M7 estimate and I1-I2 (a RSBs priori): with wavelengths less than 1 µm, and make corrections to these SDRs. For VIIRS M5-M7, the KFCDF F factors are within 0.3% Pk|k−1 = Pk−1|k−1 + Qk (4)

Update Innovation covariance T Sk = HkPk|k−1 Hk + Rk (5) Remote Sens. 2021, 13, 1075 13 of 35

Optimal Kalman Gain T −1 Kk = Pk Hk Sk (6) Updated VIIRS sensor state estimate (a posteriori) xk|k = xk|k−1 + Kk yek − Hkxk|k−1 (7)

Updated covariance estimate (a posteriori)

Pk|k = (I − Kk Hk)Pk|k−1 (8)

In the above formulations for KFCDF, xk is the estimated true VIIRS sensor F factor state and Pk is the covariance matrix of the state or error estimate.yek = (FSolar, FLunar, FDCC, FSNO)k is a heterogeneous VIIRS sensor performance measurement vector in terms of equivalent F factors from four types of measurement. In Equation (7), yek − Hkxk|k−1 is also called the innovation residual of measurements. Hk is the observation model and is set as unity after the yek and populated with equivalent F factors from diverse measurements. Qk and Rk are the covariances of the process noise and the observation noise, respectively, and are estimated with time-lagged auto-covariance. At each instant, in the predict phase, the priori estimated VIIRS F factor and covariance are used to predict VIIRS sensor state with Equations (3) and (4). Then, the VIIRS F factors derived from heterogeneous measurement are used to calculate the innovation residual with yek − Hkxk|k−1, innovation covariance and Kalman gain using Equations (5) and (6). The estimated true F factor xk|k and covariance Pk|k are updated with Equations (7) and (8), which serve as the priori for the next KFCDF update. The adaptive nature of Kalman ﬁltering enables the fusion of multiple calibration data to estimate the true VIIRS sensor calibration F factor time series, which are shown as black curves in Figure4 for VIIRS M1-M4. At each instant, the true F factor estimation provides an optimal determination of gain values in calibration. Most often, the F factor from KFCDF is aligned with lunar-F factor, as shown in Figure4, which validates the importance and robustness of lunar calibration for VIIRS RSBs. For VIIRS M1 and M3, the F factors from KFCDF start to deviate from lunar-F after 2016, when the DCC-F and SNOx-F factors became more consistent. The KFCDF scheme has been applied to recalibrate the gain coefﬁcients of VIIRS M1-M7 and I1-I2 RSBs with wavelengths less than 1 µm, and make corrections to these SDRs. For VIIRS M5-M7, the KFCDF F factors are within 0.3% of solar-F for 5 years. Since the optical throughput degradations of VIIRS M5 to M7 band varied from ~10% to ~40% due to the contamination of the mirror along the optical throughput path [4], the consistency between solar-F and KFCDF-F factors validates the reliability of solar calibration for M5-M7 bands. In Section4, the effectiveness of F factors derived from KFCDF in removing long-term biases and improving VIIRS RSB data quality is evaluated through the long-term trending of DCC data.

3.4. The Surface Roughness Rayleigh Scattering Model (SRRS) for Improving Calibration Stability (Bands with Wavelength > 1 µm) The onboard calibration of SNPP VIIRS NIR bands (M8-M11) with wavelength >1 µm relies on onboard calibration system including a solar diffuser (SD) to maintain radiometric quality and stability. During the on-orbit solar calibration, the SD panel is illuminated by sunlight for a short period. The SD is made of Spectralon and is known to degrade in reflectance at the short wavelength (blue end of the spectrum) due to exposure to solar ultraviolet (UV) and energetic particle radiation in space. For VIIRS RSBs with wavelength < 1 µm, a SD Stability Monitor (SDSM), i.e., a ratioing radiometer, is used to measure the ratio between the offset-corrected SD and sun-view digital counts, and to track SD reflectance changes. However, there is no detector in VIIRS SDSM with a center wavelength longer than 1 µm. Therefore, the onboard solar calibrations of NIR bands (M8-M11, I3) of VIIRS rely on direct measurements of reflected solar radiation from the SD through Remote Sens. 2021, 13, 1075 14 of 35

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Rotating Telescope Assembly (RTA). It is assumed that the reflectance of VIIRS SD does not degrade in the NIR bands. Therefore, the calibration factors of VIIRS NIR bands derived from onboard solar calibration do not account for the SD degradation in these where 푅0(휆) is the initial spectral reflectance of SNPP VIIRS SD and is set to be equal to NIR bands.훼(푡) On the other hand, the degradation of SNPP VIIRS NIR bands due to residual 1. 푆(휆, 푡) = contains the spectral dependence and time variation of the SD reflectance SD degradation휆4 is small and largely masked by noise, due to the weak signal and annual degradationvariations due embedded to SD in surface the sensor-roughness measurements-induced of the moon Rayleigh and DCC scattering. targets. Therefore, Here, the factor a physics-based model that links the spectral-dependent degradation in SD material with 훼(푡) is the fitting parameter derived from the measurements by eight SDSM detectors at surface roughness is used to derive correction factors for SNPP VIIRS NIR bands. discrete timeIn a previous t. The factor study, a훼 physics-based(푡) contains Surface information Roughness of the Rayleigh growth Scattering of SD (SRRS) surface roughness modelcharacterization was developed parameters to characterize. Details the spectral on the degradation derivation of of the SRRS SNPP VIIRSmodel SD can as be found in [43,44,51]monitored. Once by the the SDSM time [43 series]. In this of model, fitting the parameter long-term spectral 훼(푡) are degradation determined, of the SDEquation (2) can bereflectance used to isderive attributed the totime the- increaseddependent Rayleigh-scattering SD reflectance due degradation to the growth for in SNPP surface VIIRS NIR roughness with length scale << wavelength on the SD surface after being exposed to UV bandsand by particle plugging radiation corresponding in space. The wavelengths characteristic lengthinto Equation parameter (9). of the Figure SD surface 6b shows the degradationroughness in is SD derived reflectance from the for long-term SNPP reflectanceVIIRS NIR data bands of the, VIIRSderived SD andwith it changesthe SRRS model. The largestat approximately SD reflect theance tens of degradation nanometers level among over the VIIRS operational VNIR period bands of SNPP occurred VIIRS. at the M8 bandThis, with estimated a magnitude roughness of ~0.5% length as scale of isMarch consistent 2017. with the experimental result from the radiation exposure of a fluoropolymer sample [50], which validates the applicability Theof the SD SRRS degradation model. Furthermore, factors for the SNPP SRRS VIIRS model NIR was shownbands to shown effectively in Figure model 6b the have been appliedspectral-dependent to make corrections degradation to the of theM8 SDs-M11 on an(λ Aqua/Terra> 1 µm) radiance Moderate data. Resolution It is also Imag- noted that [52] useding Spectroradiometer a phenomenology (MODIS)-based and power SNPP/NOAA-20-law-fitting VIIRS, and derived which indicates a wavelength that the-exponent = −4.03SRRS to model model captures the spectral the governing degradation physical of process SNPP ofVIIRS spectral SD degradation, as well as [ 44making,51]. corrections to the NASASince version the spectral of VIIRS degradation NIR da of SDta. The reflectance formulation follows thein [ spectral52] is very power close law to of the phys- Rayleigh scattering, it is expected that SD of SNPP VIIRS also degrades in NIR bands with ics-basedsmaller SRRS magnitude, model. but In can an manifestearlier study its effect [2 over8], the a longer SRRS period model of time.presented The on-orbit in [43] was ap- pliedchange to estimate in VIIRS the SD’s spectral reflectance reflectance is tracked bydegradation eight detectors of withAqua wavelength MODIS

Figure 6. (a) Solar diffuser reflectance degradation over time, as monitored by eight detectors in solar diffuser stability Figure 6. (a) Solar diffuser reflectance degradation over time, as monitored by eight detectors in solar diffuser stability monitor (SDSM) (λ < 1 µm) of SNPP VIIRS. (b) Modeled degradation of SD reflectance for SNPP VIIRS near infrared (NIR) monitor (SDSM) (λ < 1 µm) of SNPP VIIRS. (b) Modeled degradation of SD reflectance for SNPP VIIRS near infrared (NIR) bands (M8-M11, λ > 1 µm) with Surface Roughness Raleigh Scattering (SRRS) model. bands (M8-M11, λ > 1 µm) with Surface Roughness Raleigh Scattering (SRRS) model. To derive the spectral degradation of SNPP VIIRS SD in NIR bands, the SRRS model 3.5. Algorithmdeveloped Improvements in [43] is used for the Thermal Emissive Bands For the thermal emissive bands, the onboard calibration α(t) with blackbody/spaceview Rm(λ, t) = R0(λ)[1 − S(λ, t)] = R0(λ) 1 − (9) and related coefficients plays a dominant role in the absoluteλ4 radiometric accuracy. Pre- vious studies have shown that modern blackbodies typically have high emissivity, which where R0(λ) is the initial spectral reflectance of SNPP VIIRS SD and is set to be equal to 1. ( ) ensuresS(λ accut) = rateα t calibration. The operational calibration of Suomi-NPP VIIRS TEB has , λ4 contains the spectral dependence and time variation of the SD reflectance generallydegradation been good due to for SD surface-roughness-inducedall nominal operations Rayleighsince launch. scattering. There Here, are the two factor major TEB calibration improvements in the Suomi-NPP VIIRS recalibration. First, the TEB calibration algorithm was improved to mitigate TEB calibration anomaly during blackbody (BB) warm-up/cool-down (WUCD) events, which were performed to characterize on-orbit calibration offset and nonlinearity changes over time. Note that the same WUCD bias correction method has also been implemented in the VIIRS ground processing system, the Interface Data Processing Segment (IDPS), since July 2019. Previ- ous studies indicate that small but persistent TEB calibration anomalies were observed during the WUCDs [19,20]. During such events, Suomi-NPP VIIRS TEBs show calibration anomalies up to 0.1 K during the cool-down phase. The VIIRS daytime sea-surface temperature (SST) product, which uses bands M15 and M16 as primary inputs, becomes anomalous, with warm spikes on the order of 0.25 K, in the SST time series [53]. For the

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α(t) is the fitting parameter derived from the measurements by eight SDSM detectors at discrete time t. The factor α(t) contains information of the growth of SD surface roughness characterization parameters. Details on the derivation of SRRS model can be found in [43,44,51]. Once the time series of fitting parameter α(t) are determined, Equation (2) can be used to derive the time-dependent SD reflectance degradation for SNPP VIIRS NIR bands by plugging corresponding wavelengths into Equation (9). Figure6b shows the degradation in SD reflectance for SNPP VIIRS NIR bands, derived with the SRRS model. The largest SD reflectance degradation among VIIRS VNIR bands occurred at the M8 band, with a magnitude of ~0.5% as of March 2017. The SD degradation factors for SNPP VIIRS NIR bands shown in Figure6b have been applied to make corrections to the M8-M11 (λ > 1 µm) radiance data. It is also noted that [52] used a phenomenology-based power-law-fitting and derived a wavelength- exponent = −4.03 to model the spectral degradation of SNPP VIIRS SD, as well as making corrections to the NASA version of VIIRS NIR data. The formulation in [52] is very close to the physics-based SRRS model. In an earlier study [28], the SRRS model presented in [43] was applied to estimate the spectral reflectance degradation of Aqua MODIS SD in NIR bands, and the gains of the Aqua MODIS NIR detectors were corrected.

3.5. Algorithm Improvements for the Thermal Emissive Bands For the thermal emissive bands, the onboard calibration with blackbody/spaceview and related coefficients plays a dominant role in the absolute radiometric accuracy. Previous studies have shown that modern blackbodies typically have high emissivity, which ensures accurate calibration. The operational calibration of Suomi-NPP VIIRS TEB has generally been good for all nominal operations since launch. There are two major TEB calibration improvements in the Suomi-NPP VIIRS recalibration. First, the TEB calibration algorithm was improved to mitigate TEB calibration anomaly during blackbody (BB) warm-up/cool-down (WUCD) events, which were performed to characterize on-orbit calibration offset and nonlinearity changes over time. Note that the same WUCD bias correction method has also been implemented in the VIIRS ground processing system, the Interface Data Processing Segment (IDPS), since July 2019. Previous studies indicate that small but persistent TEB calibration anomalies were observed during the WUCDs [19,20]. During such events, Suomi-NPP VIIRS TEBs show calibration anomalies up to 0.1 K during the cool-down phase. The VIIRS daytime sea-surface temperature (SST) product, which uses bands M15 and M16 as primary inputs, becomes anomalous, with warm spikes on the order of 0.25 K, in the SST time series [53]. For the reprocessing, the TEB calibration anomaly during WUCD was corrected using the Ltrace method and the Ltrace-2 methods [19,20] through a VIIRS SDR algorithm code change and Emissive LUT update. Both the Ltrace and the Ltrace-2 methods are designed to be localized corrections, which are only applied during the WUCD events. During the S-NPP version 2 reprocessing, the Ltrace method was applied to bands M12, M14-M16, I5, and the Ltrace-2 method was applied to bands M13 and I4, at the beginning of the S-NPP mission. Details of the Ltrace and Ltrace-2 methods are given in [19,20]. Improvements to TEB SDRs will be evaluated in Section 4.2. The second major improvement in the reprocessed TEB SDRs is the upgraded TEB radiance/BT limits since the beginning of the Suomi-NPP mission. Discrepancies between TEB radiance and BT limits (as well as reflectance for RSBs) were reported in the operational SDRs during the early Suomi-NPP mission. The issue was resolved by an Emissive Band Brightness Temperature conversion (EBBT) LUT update in the operational processing on June 22, 2015. Besides resolving the radiance/BT mismatch issue, the radiance/BT limits were also expanded in the updated EBBT LUT. For example, the M13 (a dual-gain band designed for fire detection) limits were expanded from 199–634 to 180–700 K. As a result, the M13 saturation during the early mission was reduced after reprocessing. During the version 2 reprocessing, the updated EBBT LUT was used from the beginning of the Suomi NPP mission. Remote Sens. 2021, 13, 1075 16 of 35

3.6. Recalibration Improvements for the DNB VIIRS DNB operates with three different gain stages that have the same spectral range and cover a wide radiometric dynamic range. It uses Low-Gain Stage (LGS) for daytime scenes, Mid-Gain Stage (MGS) for twilight scenes and High-Gain Stage (HGS) for nighttime scenes. DNB maintains a constant spatial resolution across the scan by dividing the entire swath into 32 aggregation zones on each side of the nadir. Although NOAA operational DNB calibration meets the specification requirements, as discussed in [54], there are some major issues with DNB operational data: (1) on-orbit calibration coefficients were not updated until late March 2012 (previously prelaunch values used); (2) no accommodation of the Relative Spectral Response (RSR) change in operational calibration resulting from telescope throughput degradation until April 2013; (3) no straylight correction until the middle of 2013; (4) atmospheric airglow contamination for the first five years of nighttime data, resulting in low absolute accuracy and substantial presence of negative radiance; (5) the presence of strong striping in radiance for higher aggregation zones (29–32) before January 2017 [54]. The DNB is radiometrically calibrated by a linear equation

L = G × (DN − DN0)/RVS (10)

where dark offset (DN0) and gain coefficient (G) are two key parameters for converting the instrument-measured digital number (DN) to radiance (L). Response Versus Scan (RVS) accounts for scan angle. The dark offsets are kept constant prior to launch. However, after launch, dark offsets of all three gain stages were updated monthly using the nighttime measurements over the Pacific Ocean during a new moon through the VIIRS recommended operating procedures (VROPs) [55]. Due to its extremely high sensitivity, DNB HGS is capable of detecting a faint airglow [54,56]. In order to correctly estimate the artificial lights for different application studies, atmospheric airglow needs to be corrected. However, due to the large variability in airglow, this is always a challenge [54,56,57]. The SDRs produced using NOAA operational calibration before December 2017 were impacted by airglow. The dark Earth scenes collected during monthly dark offset collection over Pacific Ocean (ref: VROPs) include atmospheric airglow. This resulted in the overestimation of HGS dark offset for its EV calibration. As discussed in a previous study, the impact of airglow contamination in dark offset resulted in a significant impact at a low radiance level, i.e., at 3 nW/[cm2.sr] DNB radiance level, the radiometric accuracy is degraded by more than 10% [54]. The underesti- mation in radiance by nearly 10% at 3 nW/[cm2.sr] is also explained in the study by [58]. To remove the impact of airglow in calibration, an updated method similar to [59] was developed to derive the dark offset. The technique first determines a baseline HGS dark offset using the DNB observation of deep space collected once during the spacecraft pitch maneuver early in the mission [60]. Every month, the on-orbit based offset is updated after accounting for the drift computed using the onboard Blackbody (BB) data. The HGS dark offset determined by the updated method can significantly improve low-light radiance accuracy [54]. Although the updated method has been implemented in the operational calibration since January 2017, the recalibration uses the updated method for the entire VIIRS data record (Figure7). In addition to the improvement in the absolute accuracy of low-light radiance calibration, the airglow-free dark offset resulted in a significant reduction in negative radiance pixels. Previously, the operational calibration using the old technique of offset derivation for HGS (based on Pacific Ocean until Dec. 2016) led to the majority of pixels near the new moon having a negative radiance. The removal of airglow reduced offset, which moved most of those negative radiance pixels near the new moon to the positive range [54]. As explained in [54], after using airglow-free deep-space-based dark offset, the entire VIIRS-recalibrated data quality improved with the reduction in the negative radiance pixels by more than 70% near the nadir and by more than 40% near the edge of the scan. The Remote Sens. 2021, 13, x FOR PEER REVIEW 17 of 34

radiance level, the radiometric accuracy is degraded by more than 10% [54]. The underes- timation in radiance by nearly 10% at 3 nW/[cm2.sr] is also explained in the study by [58]. To remove the impact of airglow in calibration, an updated method similar to [59] was developed to derive the dark offset. The technique first determines a baseline HGS dark offset using the DNB observation of deep space collected once during the spacecraft pitch maneuver early in the mission [60]. Every month, the on-orbit based offset is updated after Remote Sens. 2021, 13, 1075 accounting for the drift computed using the onboard Blackbody (BB) data. The HGS17 dark of 35 offset determined by the updated method can significantly improve low-light radiance accuracy [54]. Although the updated method has been implemented in the operational calibrationremaining since pixels January with negative 2017, t radianceshe recalibration occur at uses extremely the updated low radiance method levels, for the where entire the VIIRSmeasurements data record are (Figure dominated 7). by the sensor’s noise, an inherent nature of photon detection.

Figure 7. Trends in the operational DNB high-gain stage (HGS) dark offset (updated monthly) and Figure 7. Trends in the operational DNB high-gain stage (HGS) dark offset (updated monthly) and thethe reprocessed reprocessed DNB DNB HGS HGS dark dark offset offset (updated (updated weekly) weekly) [ [5544].]. The determination of DNB gain coefﬁcient is relatively complicated, due to the varying In addition to the improvement in the absolute accuracy of low-light radiance cali- dynamic range from daytime (LGS) to nighttime (HGS) observations. The LGS gain is bration, the airglow-free dark offset resulted in a significant reduction in negative radi- calibrated using the onboard SD when it is fully illuminated by the sun ance pixels. Previously, the operational calibration using the old technique of offset deri-

vation for HGS (based on Pacific Ocean until Dec.LSD 2016) led to the majority of pixels near the new moon having a negative radiance.GLGS The= removal of airglow reduced offset, which(11) dnSD moved most of those negative radiance pixels near the new moon to the positive range [5where4]. As dnexplainedSD is the in offset-corrected [54], after using LGS airglow SD response.-free deep The-space expected-based solar dark radianceoffset, the received entire VIIRSby the-recalibrated SD (LSD) is data calculated quality by improved the following with equationthe reduction in the negative radiance pixels by more than 70% near the nadir and by more than 40% near the edge of the scan. The Z cos(θ ) remaining =pixels with ×negative radiances× occur× at ×extremely× lowτ radiance× inc levelsλ , where LSD HDNB RSRDNB RVSDNB Esun BRDF SDS 2 d (12) the measurements are dominated by the sensor's noise, an inherent nature4πd of photon de-

tection.where H is the H-factor-characterizing degradation of the SD, Esun is the solar spectral irradiance,The determination BRDF is the of bidirectional DNB gain reﬂectancecoefficient is distribution relatively functioncomplicated of the, due SD, toτ SDSthe isvar- the yingtransmission dynamic functionrange from of thedaytime screen (LGS) in front to ofnighttime the SD, RSR(HGS) is theobservation relative spectrals. The LGS response gain isfunction calibrated of theusing LGS, theθ onboardinc is the SD incident when angleit is fully of sunlight illuminated arriving by the at s theun SD, and d is the distance between the Sun and the satellite in the astronomical units. Due to the wider LSD spectral response, the telescope throughputGLGS = degradation in VIIRS leads to a continuous(11) dnSD change in the DNB RSR. RSR was updated only once for operational calibration in April, wherewhich dn resultedSD is the in offset a discontinuity-corrected LGS of 3%–4% SD response. (Figure The8) in expected the LGS solar gain, radiance which eventually received bypropagated the SD (LSD to) is the calculated MGS and by HGS the following gain. However, equation recalibration used more than 50 sets of RSRs, accounting for a near continuous change in RSR over time. The details can be cos(θinc) L = ∫ H × RSR × RVS × E × BRDF × τ × dλ (12) found in [54SD]. Since bothDNB MGS andDNB HGS areDNB saturatedsun with SD view,SDS their4πd absolute2 gain is determined using the gain ratio approach: the LGS gain is transferred to the MGS gain by wheremultiplying H is the the H gain-factor ratios-characterizing of MGS/LGS, degradation and then transferred of the SD, to E thesun is HGS the gainsolar by spectral further irradiance,multiplying BRDF the gainis the ratios bidirectional of HGS/MGS. reflectance distribution function of the SD, τSDS is the transmission function of the screen in front of the SD, RSR is the relative spectral response function of the LGS, θinc is the incidentGMGS = angleGMGS/LGS of sunlight× GLGS arriving at the SD, and d is the(13) distance between the Sun and the satellite in the astronomical units. Due to the wider spectral response, the telescopeGHGS = throughputGHGS/MGS degradati× GMGS/LGSon ×in GVIIRSLGS leads to a continuous(14)

Both GMGS and GHGS are updated monthly using the DNB data collected in the twilight region. Gain ratios are determined using EV observation over the twilight region, where the gain stage transition happens. Unlike the dark offset, which changes signiﬁcantly over time for HGS, gain ratio trends are nearly constant over time [54]. The DNB imagery near the terminator is degraded by straylight, which results from the solar illumination of the instrument when the satellite passes through the day–night terminator. Straylight is seen on the night side of the terminator when the satellite moves from either from daytime to nighttime over the northern hemisphere, or from nighttime to daytime over the southern hemisphere. Monthly stray-light LUTs were prepared over Remote Sens. 2021, 13, x FOR PEER REVIEW 18 of 34

change in the DNB RSR. RSR was updated only once for operational calibration in April, which resulted in a discontinuity of 3%–4% (Figure 8) in the LGS gain, which eventually propagated to the MGS and HGS gain. However, recalibration used more than 50 sets of RSRs, accounting for a near continuous change in RSR over time. The details can be found Remote Sens. 2021, 13, x FOR PEER REVIEW 18 of 34 in [54]. Since both MGS and HGS are saturated with SD view, their absolute gain is determined using the gain ratio approach: the LGS gain is transferred to the MGS gain by multiplying the gain ratios of MGS/LGS, and then transferred to the HGS gain by further multiplying the gain ratios of HGS/MGS. change in the DNB RSR. RSR was updated only once for operational calibration in April, which resulted in a discontinuityGMGS of= 3%GMGS–4%/LGS (Figure× GLGS 8) in the LGS gain, which(13 )eventually

propagated to the MGS andGHGS HGS= G HGSgain./MGS However,× GMGS/LGS re× calibrationGLGS used more than(14) 50 sets of RSRs, accounting for a near continuous change in RSR over time. The details can be found Both GMGS and GHGS are updated monthly using the DNB data collected in the twilight inregion. [54]. GainSince ratios both are MGS determined and HGS using are EVsaturated observation with over SD tview,he twilight their region absolute, where gain is deter- minedthe gain using stage transitionthe gain ratiohappens. approach: Unlike the the dark LGS offset, gain which is transferred changes significantly to the MGS over gain by mul- tiplyingtime for HGS,the gain gain ratios ratio trends of MGS/LGS, are nearly and constant then overtransferred time [54]. to the HGS gain by further multiplying the gain ratios of HGS/MGS. Remote Sens. 2021, 13, 1075 18 of 35

GMGS = GMGS/LGS × GLGS (13)

GHGS = GHGS/MGS × GMGS/LGS × GLGS (14) 12 months, and they are reused in similar months in consecutive years. There are several earlier studies which describe, in detail, how the DNB straylight is characterized and Both GMGS and GHGS are updated monthly using the DNB data collected in the twilight corrected using a monthly LUT [61,62]. Figure9 shows a signiﬁcant improvement in theregion. DNB imageryGain ratios after are the straylightdetermined correction. using Straylight EV observation correction over was implementedthe twilight region, where the gain stage transition happens. Unlike the dark offset, which changes significantly over during operational calibration in August 2013. In the recalibration, straylight correction wastime performed for HGS, for gain the entireratio DNBtrends time are series nearly since constant launch. over time [54]. Figure 8. DNB low-gain stage (LGS) gain trends for the aggregation zone #1 (around nadir). (Left) Operational calibration showing discontinuity in LGS gain by using only one update in RSR on 4 April 2013. (Right) Recalibration showing smooth LGS gain trend after using continuous sets of time-dependent relative spectral responses (RSRs) [54].

The DNB imagery near the terminator is degraded by straylight, which results from the solar illumination of the instrument when the satellite passes through the day–night terminator. Straylight is seen on the night side of the terminator when the satellite moves from either from daytime to nighttime over the northern hemisphere, or from nighttime to daytime over the southern hemisphere. Monthly stray-light LUTs were prepared over 12 months, and they are reused in similar months in consecutive years. There are several earlier studies which describe, in detail, how the DNB straylight is characterized and cor- FigureFigure 8. 8.DNB DNB low-gain low-gain stage stage (LGS) ( gainLGS trends) gain for trends the aggregation for the aggregation zone #1 (around zone nadir). #1 (around (Left) nadir). (Left) rected using a monthly LUT [61,62]. Figure 9 shows a significant improvement in the DNB OperationalOperational calibration calibration showing showing discontinuity discontinuity in LGS gain in LGS by using gain only by oneusing update only in one RSR update on 4 in RSR on 4 imagery after the straylight correction. Straylight correction was implemented during op- AprilApril 2013. 2013. (Right (Right) Recalibration) Recalibration showing showing smooth smooth LGS gain LGS trend gain after trend using after continuous using sets continuous of sets of erational calibration in August 2013. In the recalibration, straylight correction was per- time-dependenttime-dependent relative relative spectral spectral responses responses (RSRs) [54 (].RSRs) [54]. formed for the entire DNB time series since launch. The DNB imagery near the terminator is degraded by straylight, which results from the solar illumination of the instrument when the satellite passes through the day–night terminator. Straylight is seen on the night side of the terminator when the satellite moves from either from daytime to nighttime over the northern hemisphere, or from nighttime to daytime over the southern hemisphere. Monthly stray-light LUTs were prepared over 12 months, and they are reused in similar months in consecutive years. There are several earlier studies which describe, in detail, how the DNB straylight is characterized and corrected using a monthly LUT [61,62]. Figure 9 shows a significant improvement in the DNB imagery after the straylight correction. Straylight correction was implemented during operational calibration in August 2013. In the recalibration, straylight correction was performed for the entire DNB time series since launch.

Figure 9. Sample DNB granule (Top) before and (Bottom) after straylight correction over the northern hemisphere. (Top) Operational radiance (16 December 2012) before straylight correction. (Bottom) Reprocessed radiance after straylight correction [54]. During recalibration, the prelaunch-based dark offset and gain coefﬁcients used before 20 March 2012 were also replaced by the postlaunch-based values to establish consistency. All the major calibration updates, as discussed above, have led to a consistent radiometric calibration time series with improved absolute accuracy for the entire recalibrated VIIRS DNB sensor data record. Although reprocessing has resulted in consistent calibration over the entire mission of SNPP VIIRS DNB, for long-term studies that can involve multiple sensors, it is equally important for users to understand the relative radiometric consistency with other instruments. Past studies show how DNB observations over bright stars can be used to study the relative radiometric consistency between multiple VIIRS DNB sensors [63], and trending the gain of individual VIIRS DNB sensor [64]. Another study has shown how the moon-illuminated DCC at night can be used to evaluate the radiometric consistency among DNB sensors [65]. The reprocessing of the entire DNB archive using an airglow-free dark offset has improved the absolute calibration at low-light radiance. However, the absolute calibration of the DNB sensor on-orbit for nighttime imaging is

Remote Sens. 2021, 13, 1075 19 of 35

very challenging. One such effort has been made through vicarious calibration by using a point light source in the ground [66], although the technique has large uncertainty due to atmospheric variabilities.

3.7. Geolocation Recalibration Improvements Several major S-NPP VIIRS geometric calibration parameter updates were performed prior to 22 August 2013 to reduce errors in the VIIRS I-bands, M-bands, and DNB geolocation products. Moreover, DNB terrain correction was not implemented in the SNPP operational processing until 22 May 2015, while terrain correction was implemented for I-bands and M-bands geolocation from the beginning of the mission. Details of S-NPP VIIRS geometric calibration updates in the NOAA operational processing are available in [17]. During the version 2 reprocessing, optimal versions of GEO LUTs were used: version 8 GEO PARAM LUTs were used for data before 25 April 2013 15:20 UTC and version 9 GEO LUTs were used after this time. Terrain correction was applied to DNB geolocation from the beginning of the mission. An evaluation of geolocation improvements will be presented in Section 4.3.

4. Verification and Validation of Recalibrated Data Calibration is the process of quantitatively defining the system response to known, controlled signal inputs, and validation is the process of assessing, by independent means, the quality of the data provided (CEOS http://ceos.org/ourwork/workinggroups/wgcv/ accessed on 31 November 2020). Operationally, calibration converts raw satellite measurements in counts into radiances with geophysical units using a computer system which consists of algorithms and software codes, with calibration input parameter datasets (or look-up tables). As far as recalibration is concerned, it has are three distinct sections: the raw satellite measurements or raw data record (RDR), the calibration algorithm, and the calibration input parameter datasets. Any change in the three quantities would lead to a change in the resultant radiance values. For our recalibration, the raw data record remains intact in most cases, except in the VIIRS DNB dark offset, where there is an algorithm change, which mainly occurs in the thermal emissive bands, as discussed in the previous section. Therefore, the majority of the changes in calibration occur in the input parameter datasets or LUTs, especially for the reflective solar bands (mostly captured in the F-factor or 1/gain). As a result, one approach to the verification and validation of the recalibrated data is to compare the F-factors from different versions, while validation of the calibration sensor data records can be performed over vicarious targets. In the following, we first evaluate the recalibration VIIRS RSB data over deep convective clouds. Then, the F-factors are compared between different versions.

4.1. Evaluation of Version 2 Recalibrated S-NPP VIIR RSB SDR over DCCs As discussed in earlier sections, improving the temporal stability and the absolute radiometric accuracy are the two major objectives of recalibration of VIIRS data record. To assess the improvement in data quality after recalibration, data need to be independently validated using different techniques, such as using stable vicarious sites, and deep convective clouds (DCC), lunar calibration, and comparisons with other well-calibrated sensors [4,16,23,35,48,67–69]. In this section, we used TOA reflectance trending over DCCs (after applying the Kalman-based correction) to evaluate the improvement in the temporal radiometric stability after recalibration. This study uses the mode (for VNIR bands) and mean (for SWIR bands) of monthly DCC reflectance to study the VIIRS recalibration performance. Drifts observed in the monthly PDF mode or mean values are used to analyze the calibration stability. Note that monthly DCC time series, derived using data after solar calibration, were used as one of the inputs, as well as SNOs and lunar F-factors, to generate Kalman-filter-based correction terms for VNIR band recalibration, except for M3 and M4. Strictly speaking, DCC observations are not independent of calibration for the reprocessed VNIR bands. However, Remote Sens. 2021, 13, 1075 20 of 35

DCC results may still provide some useful information about the long-term calibration stability after recalibration. Different from the VNIR bands, DCC time series were not used for the SWIR bands’ recalibration; therefore, they can be used to independently assess the recalibration improvements in these bands. Figure 10 shows bands M1-M4 of monthly DCC time series before and after recalibration. Statistics of monthly DCC time series for all VNIR bands are summarized in Table4. Our analysis shows that the recalibration is critical in reducing the residual degradation in the instrument. M1-M4 exhibit the largest improvement in temporal radiometric stability after recalibration among all VNIR bands. After recalibration, DCC-based reflectance trends indicate that the residual degradation trends are reduced to <0.3%, except for bands M3 and M4. The larger degradation for M3 and M4 could mainly be attributed to uncertainties in the DCC technique or the nonlinear calibration coefficients for the two bands (DCCs are very bright at the M3-M4 wavelengths). As we can observe in the Kalman Filtering (Figure4), unlike bands M1 and M2, DCC trending for M3 and M4 does not align well with lunar trends, and hence the Kalman Filtering algorithm places less weight on DCC trends for these bands when deriving the gain. Since the SNOx trend aligns better with lunar Remote Sens. 2021, 13, x FOR PEER REVIEW 21 of 34 trending for the M3 and M4 band, the SNOx-based time series, after recalibration, indicates better than 0.3% temporal stability for both M3 and M4. The larger trends in the DCC time series for M3 and M4 need to be further investigated in future. No significant residual degradation trends were observed in M1-M2. The results for M7 and I2 are similar to those DCC and SNOxof M1-M2. trending Small results upward indicate trends were that, observed after inrecalibration, bands M3-M5 and VIIRS I5. As reflective explained bands meet the longearlier-term (Section stability 3.3), requirement when the Kalman-filter-based, and are well gain within is derived, the two specification closely matching of input trends are assigned the major weights for gain derivation. Interestingly, unlike bands 0.3%. Moreover, bandM1s and M1 M2,–M2, DCC M7 trending–M9, for M11, band and M3-M5 I2 andare I1 stable does not to align well well within with lunar 0.1%. trending. This is a major achievementFor M3, in SNOx-basedRSB calibration desert trending and serves aligns betterthe broader with lunar user trending community than DCC. One well, of including the ocean thecolor reasons community for M3-M5, and which I1 DCC demands trending being a stringent less aligned calibration with the moon stability and SNOx re- could be the larger uncertainty in the BRDF correction. However, this needs to be further quirement of better investigatedthan 0.2%. in the future.

FigureFigure 10. 10.Suomi Suomi NPP NPP VIIRS VIIRS bands’ M1–M4bands monthly’ M1–M4 DCC monthly reﬂectance DCC time seriesreflectance before (red time color, series operational) before and (red after color,(green color)operational) recalibration. and after (green color) recalibration.

Figure 11. Suomi NPP VIIRS bands’ M8–M11 monthly DCC reflectance time series before (red color, operational) and after (green color) recalibration.

Table 4. Statistics of monthly DCC time series for the operational and reprocessed Suomi NPP VIIRS RSBs, including averaged monthly DCC reflectance (Avg.), standard deviation (SD,%), and linear trends (%/year).

Monthly DCC Reflectance Monthly DCC Reflectance

(Operational) (Reprocessed) Avg. SD (%) Trend (%/year) Avg SD (%) Trend (%/year) M1 0.949 0.8 −0.19 0.947 0.5 −0.01 M2 0.939 0.9 −0.44 0.939 0.4 −0.00 M3 0.936 0.8 −0.35 0.935 0.5 0.11 M4 0.907 0.7 −0.30 0.908 0.4 0.08 M5 0.935 0.4 −0.08 0.929 0.4 0.05 M7 0.924 0.4 0.07 0.919 0.2 0.02 M8 0.698 0.9 0.06 0.688 0.5 −0.00 M9 0.626 1.6 0.11 0.609 1.1 0.00 M10 0.232 2.9 0.11 0.228 1.7 −0.04 M11 0.371 2.1 0.09 0.368 1.3 0.00 I1 0.898 0.5 −0.14 0.900 0.4 0.05

Remote Sens. 2021, 13, 1075 21 of 35

Table 4. Statistics of monthly DCC time series for the operational and reprocessed Suomi NPP VIIRS RSBs, including averaged monthly DCC reﬂectance (Avg.), standard deviation (SD,%), and linear Remote Sens. 2021, 13, x FOR PEER REVIEW 21 of 35 trends (%/year).

Monthly DCC Reﬂectance Monthly DCC Reﬂectance DCC and SNOx trending(Operational) results indicate that, after recalibration,(Reprocessed) VIIRS reflective Trend Trend bands meet the long-termAvg. stability SD (%) requirement, and areAvg well within SD the (%) specification of (%/year) (%/year) 0.3%. Moreover, bands M1–M2, M7–M9, M11, and I2 are stable to well within 0.1%. This is a M1major achievement 0.949 in RSB0.8 calibration −and0.19 serves the 0.947 broader user 0.5 community− well,0.01 − − includingM2 the ocean 0.939 color community, 0.9 which0.44 demands a 0.939stringent calibration 0.4 stability0.00 re- M3 0.936 0.8 −0.35 0.935 0.5 0.11 quirement of better than 0.2%. M4 0.907 0.7 −0.30 0.908 0.4 0.08 M5 0.935 0.4 −0.08 0.929 0.4 0.05 M7 0.924 0.4 0.07 0.919 0.2 0.02 M8 0.698 0.9 0.06 0.688 0.5 −0.00 M9 0.626 1.6 0.11 0.609 1.1 0.00 M10 0.232 2.9 0.11 0.228 1.7 −0.04 M11 0.371 2.1 0.09 0.368 1.3 0.00 I1 0.898 0.5 −0.14 0.900 0.4 0.05 I2 0.925 0.5 −0.21 0.920 0.3 0.03 I3 0.234 3.0 −0.10 0.229 1.7 −0.05

Figure 11 shows bands M8-M11 monthly DCC time series before and after the recalibration. The statistics of the monthly DCC time series for all SWIR bands are also summarized in Table4. Our results indicate that the SRRS model (section X) performed wellFigure for 10. the Suomi recalibration NPP VIIRS purpose.bands’ M1–M4 For monthly SWIR bands,DCC reflectance the residual time se calibrationries before (red drifts are relativelycolor, operational) small compared and after (green to VNIR color) bands. recalibration. After recalibration, residual degradation trends are successfully minimized for M8-M9 and M11. M10 and I3 show relatively larger residual trends, of about −0.05%year.

Figure 11. Suomi NPP VIIRS bands’Figure 11. M8–M11 Suomi monthlyNPP VIIRS DCC bands’ reﬂectance M8–M11 time monthly series D beforeCC reflectance (red color, time operational) series before and (red after (green color) recalibration. color, operational) and after (green color) recalibration.

TableDCC 4. Statistics and SNOx of monthly trending DCC ti resultsme series indicate for the operationa that, afterl and recalibration, reprocessed Suomi VIIRS NPP reﬂective bandsVIIRS meetRSBs, including the long-term averaged stability monthly requirement, DCC reflectance and (Avg.), are wellstandard within deviation the speciﬁcation (SD,%), and of 0.3%.linearMoreover, trends (%/year). bands M1–M2, M7–M9, M11, and I2 are stable to well within 0.1%. This is a major achievementMonthly DCC in RSBReflectance calibration and servesMonthly the broader DCC Reflectance user community well,

including the ocean(Operational) color community, which demands a(Reprocessed) stringent calibration stability requirement Avg. of better SD than(%) 0.2%. Trend (%/year) Avg SD (%) Trend (%/year) M1 0.949 0.8 −0.19 0.947 0.5 −0.01 M2 0.939 0.9 −0.44 0.939 0.4 −0.00 M3 0.936 0.8 −0.35 0.935 0.5 0.11 M4 0.907 0.7 −0.30 0.908 0.4 0.08

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I2 0.925 0.5 −0.21 0.920 0.3 0.03 I3 0.234 3.0 −0.10 0.229 1.7 −0.05

4.2. F-Factor Comparison with other Independent Calibrations Remote Sens. 2021, 13, 1075 22 of 35 There are several versions of F factors for the reflective solar bands: the operational version, the latest Kalman filtering version (v2), the prior version (v1), the ocean color ver4.2.sion F-Factor (OC), Comparison and the with NASA other Independent version. CalibrationsThe differences in the F-factor between the latest versionThere in are recalibration several versions over of Fthe factors previous for the versions reflective solarrepresent bands: improvements the operational using the ad- vancedversion, themethodology latest Kalman of filtering Kalman version filtering (v2),, theas discussed prior version in (v1), Section the ocean 3.3. We color have compared theversion different (OC), and versions the NASA of F version. factors The in chronological differences in the order F-factor to betweenshow the the improvements. latest version in recalibration over the previous versions represent improvements using the advanced methodology of Kalman filtering, as discussed in Section 3.3. We have compared 4.2.1.the different Comparisons versions of between F factors inOC chronological and V1 SD order F-Factors to show the improvements. It is well known that the operational version of the F-factors has a number of incon- 4.2.1. Comparisons between OC and V1 SD F-Factors sistencies resulting from major LUT updates. This led to the development of a consistent It is well known that the operational version of the F-factors has a number of incon- versionsistencies, resultingknown fromas version major LUT 1 (V1). updates. However, This led toV1 the recalibration development of was a consistent still solely based on onboardversion, known solar asdiffuser version calibration. 1 (V1). However, The V1deficiency recalibration of this was approach still solely basedis revealed on when com- paredonboard with solar the diffuser OC calibration.version (wh Theich deficiency incorporates of this approach the lunar is revealedcalibration when, especially com- in bands M1pared-M4), with Figure the OC version12. A key (which weakness incorporates of using the lunar onboard calibration, solar especially diffuser in bandscalibration alone is M1-M4), Figure 12. A key weakness of using onboard solar diffuser calibration alone is that residual degradation is not accounted for by the short wavelength bands. For exam- that residual degradation is not accounted for by the short wavelength bands. For example, ple,the F-factor the F-factor ratio between ratio between these two canthese be largertwo can than be 2%, larger which than means 2%, that which the residual means that the re- sidualdegradation degradation of not using of the not lunar using calibration the lunar can calibration be >2% in the can long-term be >2% trend. in the long-term trend.

Figure 12. 12.F-factor F-factor differences differences between between V1 and V1OC. and M1-M4 OC. M1 bands-M4 show bands long-term show long calibration-term calibration dif- ferencedifferencess resulting resulting fromfrom lunar-based lunar-based OC calibration.OC calibration. 4.2.2. Comparison between V1, OC and V2 F-Factors 4.2.2.Recognizing Comparison this issuebetween of residual V1, OC degradation, and V2 theF-Factors VIIRS SDR team developed a new and comprehensiveRecognizing approachthis issue to of incorporate residual notdegradation, only lunar, butthe also VIIRS other SDR independent team developed a new andcalibration comprehensive information, approach such as using to aincorporate deep convective not cloud,only lunar, desert, andbut SNOs.also other All independent independent calibration is synthesized using the Kalman filtering approach, which is the calibrationlatest version information, presented in this such paper as (referred using a to deep as V2). convective Figure 13 shows cloud, that desert, the F-factor and SNOs. All in- dependentratios are significantly calibration reduced is synthesized between V2 and using OC. In the addition, Kalman ratio filtering trends are approach nearly flat , which is the latestindicating version more presented stable calibration in this over paper the mission (referred life. Noteto as that V2). high Figure M5 and 13 M7show biasess that the F-factor ratiosare accounted are significantly for in the V2, butreduced not accounted between for inV2 OC, and and OC. this standsIn addition, out in the ratio F-factor trends are nearly ratio. Solar irradiance model differences, which give static differences in each band, as flat indicating more stable calibration over the mission life. Note that high M5 and M7 explained in Section 3.1, are not reflected in these comparisons. biases are accounted for in the V2, but not accounted for in OC, and this stands out in the F-factor ratio. Solar irradiance model differences, which give static differences in each band, as explained in Section 3.1, are not reflected in these comparisons.

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FigureFigure 13. 13.Differences Differences between between SD F-factors SD F- withfactors Kalman with filter Kalman correction filter (V2) correction and OC SD (V2) F-factors. and OC SD F-factors.The calibration The calibration differences differences for M1-4 are for within M1- 2%4 are level, within nearly 2% mitigated level, afternearly the mitigated correction. after the correction. 4.2.3. Comparisons between NASA F-Factors and NOAA V2 F-Factors 4.2.3.The Comparisons static differences between between NASA the NASA F-Factors and NOAA and V2 NOAA F-factors V2 are F- mostlyFactors caused by theThe solar static irradiance differences differences. between NASA the F-factors NASA are and based NOAA on the V2 MODTRAN F-factors solar are mostly caused irradiance model, whereas NOAA V2 F-factors use the Thuillier 2003 model, as mentioned bypreviously. the solar Figure irradiance 14 shows differences. an F-factor in NASA each band, F-factors with detector are based 1 and on HAM the side MODTRAN 1 solar irradiancein high- (or single-)model, gain whereas state. NOAA The starting V2 F point-factors differences use the are Thuillier caused by 2003 the Emodel,sun, as as mentioned previously.shown in Figure Figure1. The 14 additional shows statican F- differencesfactor in each are observed band, inwit theh shortdetector wavelength 1 and HAM side 1 in bands (M1-M4) in Figure 14a,c below. These differences are caused by the different filtering high- (or single-) gain state. The starting point differences are caused by the Esun, as shown results from NASA and NOAA. NASA F-factors are biased to the initial two lunar F-factors in Figure 1. The additional static differences are observed in the short wavelength bands that provided a greater increase in the first year of F-factors, as shown in Figure 14a,c [28]. (M1On the-M4) other in hand,Figure NOAA 14a,c version below. 2 F-factorsThese dif areferences derived usingare caused the combination by the different of the filtering re- sultslunar, from DCC, SNOxNASA and and SD NOAA. trends, as NASA shown inF- Figurefactors4. are biased to the initial two lunar F-factors that Theprovided F-factors a greater are very increase similar for in bands the first M6, M7,year M8 of andF-factors I2, due, toas theshown very smallin Figure 14a,c [28]. Onsolar the irradiance other hand, differences NOAA of less version than 0.2%. 2 F The-factors long-term are derived trends are using also very the similar combination of the in bands M5, I1 (Figure 14b) and M9-11 and I3, except for the solar irradiance differences. lunar,The large DCC, detector SNOx responsivity and SD changes trends in, as these shown bands in are Figure well-captured 4. by the NASA and NOAA F-factors. As discussed earlier, a major improvement in VIIRS data after recalibration is the increased radiometric accuracy. Past studies on NOAA VIIRS operational data indicate that the radiometric bands’ M5 and M7 calibration was overestimated by 1.5% and 2%, respectively [33,34,70]. The rest of the RSBs suggest absolute calibration accuracy well within the specification of 2%. One exception is the VIIRS band M11, which does not meet the above specification and has a waiver. Similarly, imagery band I2, which spectrally matches with M7, also suggests a bias on the same order. Since these biases are not time- varying, a constant scaling in the recalibrated radiance/reflectance can directly improve the data quality. The scaling factors for M5, M7, and I2 are incorporated into the Kalman- Filtering-based bias correction value to account for the increased absolute calibration. Thus, for these three bands, both residual drift correction and absolute accuracy correction are performed in the recalibrated data.

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Figure 13. Differences between SD F-factors with Kalman filter correction (V2) and OC SD F-factors. The calibration differences for M1-4 are within 2% level, nearly mitigated after the correction.

4.2.3. Comparisons between NASA F-Factors and NOAA V2 F-Factors The static differences between the NASA and NOAA V2 F-factors are mostly caused by the solar irradiance differences. NASA F-factors are based on the MODTRAN solar irradiance model, whereas NOAA V2 F-factors use the Thuillier 2003 model, as mentioned previously. Figure 14 shows an F-factor in each band, with detector 1 and HAM side 1 in high- (or single-) gain state. The starting point differences are caused by the Esun, as shown in Figure 1. The additional static differences are observed in the short wavelength bands (M1-M4) in Figure 14a,c below. These differences are caused by the different filtering results from NASA and NOAA. NASA F-factors are biased to the initial two lunar F-factors that provided a greater increase in the first year of F-factors, as shown in Figure 14a,c [28]. Remote Sens. 2021, 13, 1075 24 of 35 On the other hand, NOAA version 2 F-factors are derived using the combination of the lunar, DCC, SNOx and SD trends, as shown in Figure 4.

Figure 14. F-factor comparison plots between NASA F-factors (solid lines) and NOAA version 2 F-factors (dotted lines) in the RSB bands.

4.3. Evaluation of Version 2 Reprocessed S-NPP VIIR TEB SDRs The Suomi NPP version 2 reprocessed TEB SDRs were evaluated using independent CrIS observations. Figure 15 shows daily averaged VIIRS-CrIS BT difference time series for bands I5, M13, and M15-M16 for the period from March 2012 to February 2017. The time series were generated using the reprocessed Suomi NPP VIIRS TEB SDRs and the reprocessed CrIS normal spectral resolution LWIR (overlapping with VIIRS M15-M16 and I5) and SWIR (overlapping with M13) SDR. It can be observed that VIIRS agrees with CrIS on the order of 0.1 K for all four bands. VIIRS bands I5 and M15-M16 agree with CrIS within 0.05 K. VIIRS M13 shows slightly larger biases relative to CrIS, ~0.1 K. Our analyses indicate that the larger M13 bias is mostly due to the coarse spectral resolution of the CrIS SWIR normal spectra data used [19]. M13 VIIRS–CrIS biases will be reduced by 0.09 K if the reprocessed CrIS full spectra SDRs are used. However, CrIS full-spectra RDRs are not available for the entire Suomi NPP mission. Therefore, they are not used in the long-term analysis in this study. Note that the spectral resolutions of CrIS LWIR spectra are the same in the normal- and full-spectra SDRs. Downward trends were observed in the VIIRS–CrIS BT difference time series. However, the trends are very small, about −0.001, −0.003, −0.002 and −0.002 K/year for I5, M13, and M15-M16, respectively. Residual WUCD biases after the WUCD bias correction are ~0.01 K in the reprocessed TEB SDRs, which is signiﬁcantly reduced compared to the up to 0.1 K bias (for M15) observed in the NOAA operational products. We also evaluated the TEB SDR improvement due to the use of the upgraded TEB radiance/BT limits (see Section 3.5). Figure 16 compares M13 aggregated radiance (March 11, 2014, 16:47 UTC, saturation occurs over sun glint) before (operational) and after the version 2 reprocessing. In the NOAA operational processing, two M13 SDR pixels are assigned to the minimum radiance, due to the fact that all un-aggregated sub-pixels are either saturated or beyond the EBBT LUT-deﬁned limits. Because of the improved EBBT radiance/BT limits, improved radiances were produced using the unsaturated sub-pixels, and the two pixels became more consistent with the surrounding, good-quality pixels after the recalibration. Remote Sens. 2021, 13, x FOR PEER REVIEW 24 of 34

Figure 14. F-factor comparison plots between NASA F-factors (solid lines) and NOAA version 2 F- factors (dotted lines) in the RSB bands.

The F-factors are very similar for bands M6, M7, M8 and I2, due to the very small solar irradiance differences of less than 0.2%. The long-term trends are also very similar in bands M5, I1 (Figure 14b) and M9-11 and I3, except for the solar irradiance differences. The large detector responsivity changes in these bands are well-captured by the NASA and NOAA F-factors. As discussed earlier, a major improvement in VIIRS data after recalibration is the increased radiometric accuracy. Past studies on NOAA VIIRS operational data indicate that the radiometric bands’ M5 and M7 calibration was overestimated by 1.5% and 2%, respectively [33,34,70]. The rest of the RSBs suggest absolute calibration accuracy well within the specification of 2%. One exception is the VIIRS band M11, which does not meet the above specification and has a waiver. Similarly, imagery band I2, which spectrally matches with M7, also suggests a bias on the same order. Since these biases are not time- varying, a constant scaling in the recalibrated radiance/reflectance can directly improve the data quality. The scaling factors for M5, M7, and I2 are incorporated into the Kalman- Filtering-based bias correction value to account for the increased absolute calibration. Thus, for these three bands, both residual drift correction and absolute accuracy correction are performed in the recalibrated data.

4.3. Evaluation of Version 2 Reprocessed S-NPP VIIR TEB SDRs The Suomi NPP version 2 reprocessed TEB SDRs were evaluated using independent CrIS observations. Figure 15 shows daily averaged VIIRS-CrIS BT difference time series for bands I5, M13, and M15-M16 for the period from March 2012 to February 2017. The time series were generated using the reprocessed Suomi NPP VIIRS TEB SDRs and the reprocessed CrIS normal spectral resolution LWIR (overlapping with VIIRS M15-M16 and I5) and SWIR (overlapping with M13) SDR. It can be observed that VIIRS agrees with CrIS on the order of 0.1 K for all four bands. VIIRS bands I5 and M15-M16 agree with CrIS within 0.05 K. VIIRS M13 shows slightly larger biases relative to CrIS, ~0.1 K. Our analyses indicate that the larger M13 bias is mostly due to the coarse spectral resolution of the CrIS SWIR normal spectra data used [19]. M13 VIIRS–CrIS biases will be reduced by 0.09 K if the reprocessed CrIS full spectra SDRs are used. However, CrIS full-spectra RDRs are not available for the entire Suomi NPP mission. Therefore, they are not used in the long-term analysis in this study. Note that the spectral resolutions of CrIS LWIR spectra are the same in the normal- and full-spectra SDRs. Downward trends were observed in the VIIRS–CrIS BT difference time series. However, the trends are very small, about −0.001, −0.003, −0.002 and −0.002 K/year for I5, M13, and M15-M16, respectively. Residual WUCD biases after the WUCD bias correction are ~0.01 K in the reprocessed TEB SDRs, which is significantly Remote Sens. 2021, 13, 1075 reduced compared to the up to 0.1 K bias (for M15) observed in the NOAA25 of operational 35 products. Remote Sens. 2021, 13, x FOR PEER REVIEW 25 of 34

Figure 15. Daily averaged VIIRS–CrIS BT difference time series (March 2012 to February 2017). Reprocessed VIIRS and CrIS normal spectra data at nadir were used.

We also evaluated the TEB SDR improvement due to the use of the upgraded TEB radiance/BT limits (see Section 3.5). Figure 16 compares M13 aggregated radiance (March 11, 2014, 16:47 UTC, saturation occurs over sun glint) before (operational) and after the version 2 reprocessing. In the NOAA operational processing, two M13 SDR pixels are assigned to the minimum radiance, due to the fact that all un-aggregated sub-pixels are either saturated or beyond the EBBT LUT-defined limits. Because of the improved EBBT radiance/BT limits, improved radiances were produced using the unsaturated sub- pixels, Figureand the 15. twoDaily pixels averaged became VIIRS–CrIS more BTconsistent difference with time the series surrounding (March 2012, to good February-quality 2017). pixels af- Reprocessedter the recalibration. VIIRS and CrIS normal spectra data at nadir were used.

Figure 16. Comparison of M13 radiance (11 March 2014, 16:47 UTC) before (operational) and after Figure 16. Comparison of M13 radiance (11 March 2014, 16:47 UTC) before (operational) and after the version 2 reprocessing. the version 2 reprocessing. 4.4. Evaluation of Version 2 Reprocessed S-NPP VIIRS Geolocation Products 4.4. EvaluationMajor improvements of Version in2 Reprocessed the geolocation S-NPP products VIIRS from Geolocation February Products 2012 to December 2016 wereMajor evaluated improvements in our previous in the geolocation study over a products sample area from located February in Northwestern 2012 to December Africa2016 were (for I-bands’/M-bands’ evaluated in our geolocation)previous study and DNBover nighttimea sample pointarea located sources (forin North DNB western geolocation)Africa (for I [-17bands]. I-bands’’/M-bands and’ M-bands’ geolocation) geolocation and DNB errors nighttime were evaluated point sources using the (for DNB control point-matching (CPM) program developed by the National Aeronautics and Space geolocation) [17]. I-bands’ and M-bands’ geolocation errors were evaluated using the con- Administration (NASA) using 1000+ globally distributed Landsat red band (0.65–0.67 µm) trol point-matching (CPM) program developed by the National Aeronautics and Space Administration (NASA) using 1000+ globally distributed Landsat red band (0.65 – 0.67 µm) ground-control point chips [17]. Results show that all short-term geolocation anomalies from February 2012 to August 2013 were effectively minimized after reprocessing. Averaged DNB geolocation errors are within 0.5 pixels (0.375 km) and standard deviations are about 0.2 km. After the implementation of DNB terrain correction, DNB geolo-

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ground-control point chips [17]. Results show that all short-term geolocation anomalies fromcation February errors at 2012 off- tonadir August high 2013 elevation were effectively location minimizeds were reduced after reprocessing.from ~9 to ~0.375 Aver- km. Re- agedsults DNBfrom geolocation the previous errors study are can within also 0.5 be pixels applied (0.375 to km)the andversion standard 2 reprocessed deviations geolocation are aboutproducts. 0.2 km. After the implementation of DNB terrain correction, DNB geolocation errors at off-nadirIn this high study, elevation we further locations evaluated were reduced the entire from ~9reprocessed to ~0.375 km. I-bands Results’ geolocation from the data previous study can also be applied to the version 2 reprocessed geolocation products. records using the NASA CPM program. Figure 17 shows I-bands’ geolocation errors in In this study, we further evaluated the entire reprocessed I-bands’ geolocation data recordsthe along using-scan the and NASA along CPM-track program. directions Figure from 17 shows January I-bands’ 2012 geolocationto February errors 2017. in It can be theobserved along-scan that andthe along-trackreprocessed directions I-bands’ from geolocation January 2012product to February performed 2017. well, It can with be errors observedof ~6 ± 81 that m the(along reprocessed-scan) and I-bands’ 9 ± 93 geolocation m (along product-track). performed Two short well,-anomalies, with errors which of were ~6not± covered81 m (along-scan) by our previous and 9 ± 93 study m (along-track). [17], were Two observed short-anomalies, in the reprocessed which were geolocatio not n coveredproduct. by The our first previous short study-term [ 17anomaly], were observed lasts for in 2- thedays reprocessed (5–6 January geolocation 2012), caused product. by miss- Theing ﬁrstGPSshort-term data. The anomalysecond short lasts for-term 2-days anomaly (5–6 January occurs 2012), on 19 caused August by 2015 missing (around GPS 14:20– data.21:12 Theh UTC), second caused short-term by a spacecraft anomaly occurs control on computer 19 August clock 2015 (around error (drifted 14:20–21:12 relativ h e to the UTC), caused by a spacecraft control computer clock error (drifted relative to the GPS time). GPS time). Note that the VIIRS I-bands and M-bands are well co-registered, band-by- Note that the VIIRS I-bands and M-bands are well co-registered, band-by-band, with 2 × 2 I-bandband, with pixels 2 nested× 2 I-band to one pixels M-band nested pixel. to I-bands’ one M- geolocationband pixel. error I-bands results’ geolocation can also be error re- appliedsults can to also M-bands. be applied These twoto M short-term-bands. These anomalies two short will be-term studied anomalies and mitigated will be in studied the and futuremitigated reprocessing. in the future reprocessing.

Figure 17.17.S-NPP S-NPP VIIRS VIIRS I-bands’ I-bands geolocation’ geolocation errors errors in the in along-scan the along (top-scan) and (top along-track) and along (bottom-track) (bot- directionstom) directions for the for version the version 2 reprocessed 2 reprocessed geolocation geolocation products. products. 5. Conclusions 5. Conclusions This study provides a comprehensive analysis of the recalibration algorithms, processes,This upgrades, study andprovides procedures a com forprehensive the recalibration analysis and of reprocessing the recalibration of the Suomi algorithms, NPP pro- VIIRScesses, sensor upgrades, data records. and procedures In the recalibration, for the recalibration we resolved and inconsistencies reprocessing in the of process-the Suomi NPP ingVIIRS algorithms, sensor data terrain records. correction In andthe straylightrecalibration, correction, we resolved and anomalies inconsistencies in the thermal in the pro- bands.cessing To algorithms, improve the terr stabilityain correction of the reflective and straylight solar bands, correction, we developed and aanomalies Kalman filter- in the ther- ingmal model bands. to To incorporate improve onboardthe stability solar, of lunar, the reflective desert site, solar inter-satellite bands, we calibration, developed and a Kalman deepfiltering convective model cloudto incorporate calibration onboard methodology. solar, We lunar, further desert developed site, inter and implemented-satellite calibration, the solar diffuser surface roughness Rayleigh scattering model to account for the sensor and deep convective cloud calibration methodology. We further developed and imple- responsivity degradation in the short-wave infrared bands. The recalibrated radiometric datasetmented (namely, the solar the diffuser level 1b surface or sensor roughness data records) Rayleigh also incorporates scattering bias model corrections to account for for the thesensor reflective responsivity solar bands, degradation which not in only the addressesshort-wave known infrared calibration bands. biases,The recalibrated but also radi- allowsometric alternative dataset (namely calibration, the to level be applied 1b or sensor if so desired. data records) The recalibrated also incorporates datasetwas bias correc- validatedtions for usingthe reflective vicarious solar sites and bands, alternative which methods, not only and addresses compared known with independent calibration biases, processingbut also allows from otheralternative organizations. calibration The recalibratedto be applied data if so were desired. proven The to be recalib of superiorrated dataset quality,was validated with improved using radiometric/geometricvicarious sites and alternative stability and methods, accuracy. Theand recalibrated compared data with inde- arependent now available processing from from 2012 other to 2020 organizations. for all users, and The will recalibrated eventually data be archived were proven in the to be of NOAA CLASS database. superior quality, with improved radiometric/geometric stability and accuracy. The recalibrated data are now available from 2012 to 2020 for all users, and will eventually be archived in the NOAA CLASS database.

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Author Contributions: Conceptualization, C.C.; methodology, C.C.; software, B.Z.; validation, X.S., W.W., S.U., T.C., Y.G., Y.B. and S.B.; formal analysis, X.S., W.W., S.U., T.C. and S.B.; investigation, B.Z., X.S., W.W., S.U., T.C. and S.B.; resources, C.C., L.L., S.K.; data curation, B.Z.; writing—original draft preparation, C.C.; writing—review and editing, C.C., B.Z., X.S., W.W., S.U., T.C., Y.G., S.B., L.L. and S.K.; supervision, C.C. and S.K.; project administration, S.K.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies -CISESS) at the University of Maryland, Earth System Science Interdisciplinary Center (UMD ESSIC). Acknowledgments: Authors would like to thank NASA VIIRS Calibration Support Team (VCST) for providing NASA SD F-factors for the long-term trend comparisons. The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the author(s) and do not necessarily reflect those of NOAA or the Department of Commerce. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A. Recalibration Processing System with High-Performance Computing The recalibration and algorithm improvements discussed in this paper provide the foundation for reprocessing. On the other hand, actual VIIRS SDR reprocessing is a major challenge due to its large data volume. Each day, about 1012 VIIRS RDRs (55GB) are processed into about 30,000 SDR ﬁles, with a total volume about 540 GB after compression, which also requires a high number of CPU hours. These large data volume and high CPU demands become a storage and processing bottleneck for reprocessing. For faster reprocessing of VIIRS SDR, an embarrassingly parallel (also called perfectly parallel) scheme has been developed for the Algorithm Development Library (ADL), which is a comprehensive ground-processing package that mirrors the ofﬁcial IDPS system for JPSS, using the super-computer clusters at University of Maryland (Figure1).

A.1. Super Computer System and Software for the Reprocessing In embarrassingly parallel computing, we equally distribute ~1012 VIIRS SDR reprocessing calculations of each day into different cluster nodes/CPUs, so that we can utilize the multiple nodes and CPUs in the cluster efficiently. Besides the 55 GB RDR files, and the 540 GB output SDR files, there are millions of interim files of more than 1TB in volume that are generated/deleted in converting RDR to SDR for one-day VIIRS datasets. One bottleneck in the multiple granule calculation is the data transferal between worker nodes and the main storage. We have designed the code to utilize a local disk on each node and temporary file storage in RAM memory. In this way, we can avoid a heavy burden on hard drives due to massive and frequent writing/reading on the local and main storage. The bandwidth (56 Gbps) for main storage, and the memory size of each computing node, have been considered for optimized CPU numbers. The supercomputer used for this work is a cluster with 39 computing nodes, each with 24 CPUs (Figure A1). Its main storage was expanded to more than 1PB with an infinite bandwidth (56 Gbps) connected to slave nodes. In addition, transferring all VIIRS RDRs of the reprocessing period (~20 TB per year) from NOAA to the UMD supercomputer took a few weeks. An ADL Block 2 (Version 5.3.19 with IDPS algorithm: 01.00) is used for the reprocessing. A major upgrade of this version is the use of the new NOVAS software, which leads to a 0.00001-degree improvement in latitude/longitude, or less than a meter in geolocation. In ADL BLK2, the SQLite database is also used to manage all kinds of inputs, outputs and intermediate files. In addition, the ADL code was upgraded for thermal-band radiance bias correction for a Warm-Up Cool-Down event. The Warm-Up Cool-Down algorithm is based on [19], which was implemented in the IDPS VIIRS SDR operational processing in 2019. For the best processing efficiency, we allocated each computing node with one day’s VIIRS RDR to fully use the CPU and memory. The ADL processing for geolocation and radiometric calibration are separated. Each has a different memory requirement and, hence, Remote Sens. 2021, 13, 1075 28 of 35

the optimal adjustment of the CPU numbers for different processes is needed. Figure A2 shows a ﬂow chart of the VIIRS SDR processing and time estimation on the bamboo cluster. Basically, using 18 computing nodes, one-year VIIRS SDR can be processed within an 8-day timeframe.

A.2. Calibration Parameter Input Datasets/Lookup Tables, Raw Data, and Data Volume Reduction The radiometric and geolocation products for every band have frequent look-up table updates in the operations, to reflect the latest calibration changes, especially in the first few years. A selected list of the major LUTs updates used in operational SDR products is provided in AppendixB. Some of these LUT changes can be trivial, such as changes in the data format, while other changes can significantly affect the radiometric and geolocation accuracy. In the reprocessing, the latest and most mature recalibrated LUTs were used, which could be significantly different from what is used in the operations. Handling VIIRS datasets can be frustrating, due to their large volume and limited disk space, I/O speed, network speed, etc. Early IDPS products did not use compression and, hence, the total daily VIIRS SDR file volume is extremely large, at more than 1.7 TB as of early 2014. During reprocessing, we apply H5 GZIP compression by modifying the ADL code to reduce the daily SDR datasets to 540GB. Figure A3 shows the data volume analysis of the daily reprocessed VIIRS SDR products (compressed in the same way as IDPS products after 2014). During reprocessing, we reduced the total volume size by removing the scarcely needed datasets. For example, the geolocation products mainly include the ellipsoid geolocation and terrain-corrected geolocations, and take up almost 50% of the total data storage. The ellipsoid geolocation takes up about 26%. The ellipsoid geolocation is not the true geolocation on the earth’s surface and is seldom used in EDR teams, and can be derived from terrain-corrected geolocation products, and hence the ellipsoid geolocation data were removed from reprocessing products. This can save at least 26% of disk space. Other intermediate products, such as VIIRS Calibrated Dual-Gain Band (IVCDB), are not kept, to save more space. Reprocessing these products later can be achieved through on-demand reprocessing for specific area/time. Through eliminating those scarcely needed datasets, we have achieved a total daily volume of about 340 Gb/day (about 120 TB/year).

A.3. Multiple Versions of RDR Consideration Multiple versions of VIIRS RDRs can be found from NOAA/CLASS VIIRS SDR repository, such as versions of A1, A2, A3 for some granules. Generally, the higher the version of the RDRs, the more complete the information it stores. A general rule in reprocessing is that we use the highest version (or latest version) of RDRs for the same granule if multiple versions of RDRs exist, to avoid confusion. Some RDRs are missing from the RDR repository in either NOAA CLASS or GRAVITE, or do not have enough observation or calibration information. These missing RDRs can cause SDR gaps.

A.4. Data Format The reprocessed VIIRS SDR dataset contains compressed HDF5 files, with GZIP compression inside H5 file. The name convention follows the ADL definition of HDF5 files similar to IDPS. Currently, data are stored as single-granule H5 files. However, if these data are transferred to an NCEI/NOAA CLASS data server, they will be concatenated using the NAGG software, combining every four granules into one file for each band or geolocation. The naming convention of the data still follows the IDPS definition, with the name in the following format.

A.5. Data Distribution The initial goal for the VIIRS SDR data distribution was to transfer it to NOAA/NCEI CLASS for data distribution. However, the large volume of data makes this complicated, especially regarding data storage and public access. We created a temporary data distribution ftp site with direct access to the UMD bamboo data storage. A simple data selection Remote Sens. 2021, 13, x FOR PEER REVIEW 29 of 34

especially regarding data storage and public access. We created a temporary data distri- Remote Sens. 2021, 13, x FOR PEER REVIEW 29 of 34 Remote Sens. 2021, 13, 1075 bution ftp site with direct access to the UMD bamboo data storage. A simple29 of data 35 selection web interface was also provided, for users to select their desired granules based on the timeespecially of interest regarding and geographicdata storage and windows. public access. Temporary We created web a tempor accessary to data the distri- data is provided throughbutionweb interface ftpthe site following with was direct also link provided, access: https://ncc.nesdis.noaa.gov/VIIRS/index.php to the for UMD users bamboo to select data their storage. desired A granules simple data based selection on (access the ed on 31 time of interest and geographic windows. Temporary web access to the data is provided Augustweb interface 2020). Thiswas alsois used provided to tak, fore user users requests to select their for data desired at presentgranules , basedand may on the change in the through the following link: https://ncc.nesdis.noaa.gov/VIIRS/index.php (accessed on time of interest and geographic windows. Temporary web access to the data is provided future.31 August 2020). This is used to take user requests for data at present, and may change in through the following link: https://ncc.nesdis.noaa.gov/VIIRS/index.php (accessed on 31 the future. August 2020). This is used to take user requests for data at present, and may change in the future.

FigureFigure A1. A1. SupercomputerSupercomputer Configuration Conﬁguration at the atthe University University of Maryland, of Maryland, Earth Earth System System Science Science InterdisciplinaryInterdisciplinary Center Center (UMD (UMD ESSIC) ESSIC).. Figure A1. Supercomputer Configuration at the University of Maryland, Earth System Science Interdisciplinary Center (UMD ESSIC).

Figure A2. Flow-chart of VIIRS sensor data record (SDR) reprocessing using the Supercomputer bamboo at the University of Maryland, Earth System Science Interdisciplinary Center (UMD ESSIC)

Remote Sens. 2021, 13, x FOR PEER REVIEW 30 of 34

Remote Sens. 2021Figure, 13, 1075 A2. Flow-chart of VIIRS sensor data record (SDR) reprocessing using the Supercomputer30 of 35 bamboo at the University of Maryland, Earth System Science Interdisciplinary Center (UMD ESSIC)

Figure A3. Daily VIIRS SDR Average Data Volume (GB) for each type of SDR ﬁle (1013 granules). Figure A3. Daily VIIRS SDR Average Data Volume (GB) for each type of SDR file (1013 granules). A.6. OnDemand Reprocessing Appendix A.6. OnDemandAs discussed Reprocessing previously, a major challenge in the VIIRS SDR dataset is its data volume. As discussedThe previously, reprocessed VIIRS a major SDR fromchallenge 2012 to in 2020 the requires VIIRS ~1 SDR Petabyte dataset of data is storage.its data This vol- is ume. The reprocesa challengesed VIIRS even SDR with from today’s 2012 storage to 2020 capabilities. requires On the~1 Petabyte other hand, of the data input storage. data for the processing system are relatively smaller. The data volume ratio between output SDR This is a challengeand inputeven RDR with is intoday’s the order storage of 10:1. capabilities. Additionally, theOn data the canother very hand, often be the generated input data for the processingquickly, as system they are are downloaded relative orly transmittedsmaller. The over data the network. volume This ratio led usbetween to experiment output SDR and inputwith aRDR new reprocessingis in the order concept, of 10:1. called Additionally OnDemand reprocessing., the data can very often be generated quickly, asThe they basic are concept downloaded of OnDemand or transmitted reprocessing is over simple: the we network. do not need This to generateled us all the SDRs at once. Instead, we can generate them as they are needed. This can effectively to experiment withaddress a new the data reprocessing volume and concept, storage issue. called In what OnDemand follows, we reprocessing. discuss a scenario where The basic conthiscept method of OnDemand can be used. reprocessing is simple: we do not need to generate all the SDRs at once.A Instead, remote enterprise we can generate user is interested them inas a they particular are needed. time-period This of reprocessedcan effectively VIIRS address the dataSDR volume over a particularand storage location. issue. In thisIn what scenario, follows, the user we has discuss two choices a scenario under the whe schemere this method canof be OnDemand used. Reprocessing: A remote a. enterpriseThe VIIRS user SDR is team interested could regenerate in a particular the dataset time from-period scratch using of reprocessed the raw data record requested by the user, and provide the data to the user through the network. VIIRS SDR over a particularWhen this location. is done, the In reprocessed this scenario, SDR data the canuser be has removed two fromchoices the server under so the they scheme of OnDemanddo Reprocessing: not take up space; a. The VIIRS b.SDR Alternatively,team could theregener VIIRSate SDR the team dataset can obtain from an scratch account onusing the user’sthe raw computer, data install the processing software ADL, and the input ﬁles as well as the raw data record requestedrecords. by the Then, user, the and VIIRS provide SDR data the can data be produced to the user on the through user’s computer. the network. When this is done,It is recognizedthe reprocessed that OnDemand SDR data reprocessing can be removed highly depends from the on server the coordination so they do not takebetween up space the; VIIRS SDR team and the enterprise user. This method may not work for b. Alternatively,everyone, the VIIRS especially SDR ad hocteam users. can Nevertheless, obtain an account this does addresson the theuser’s data volumecomputer, issue install the andprocessing it has been software successfully ADL, tested and multiple the input times. files as well as the raw data records. Then, the VIIRS SDR data can be produced on the user’s computer. It is recognized that OnDemand reprocessing highly depends on the coordination between the VIIRS SDR team and the enterprise user. This method may not work for everyone, especially ad hoc users. Nevertheless, this does address the data volume issue and it has been successfully tested multiple times.

Appendix B. Look Up Table Updates in the Operational Datasets VIIRS-SDR-GEO-DNB-PARAM-LUT; VIIRS-SDR-GEO-IMG-PARAM-LUT; 23 February 2012 VIIRS-SDR-GEO-MOD-PARAM-LUT;

Remote Sens. 2021, 13, 1075 31 of 35

Appendix B. Look Up Table Updates in the Operational Datasets

VIIRS-SDR-GEO-DNB-PARAM-LUT; 23 February 2012 VIIRS-SDR-GEO-IMG-PARAM-LUT; VIIRS-SDR-GEO-MOD-PARAM-LUT; VIIRS-SDR-DELTA-C-LUT; 29 February 2012 VIIRS-SDR-GAIN-LUT; VIIRS-SDR-BB-TEMP-COEFFS-LUT; 07 March 2012 VIIRS-SDR-DELTA-C-LUT; VIIRS-SDR-GAIN-LUT; VIIRS-SDR-RADIOMETRIC-PARAM-LUT; VIIRS-SDR-TELE-COEFFS-LUT; VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 15 March 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 29 March 2012 VIIRS-SDR-GEO-DNB-PARAM-LUT; 24 April 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 25 April 2012 VIIRS-SDR-RADIOMETRIC-PARAM-LUT; 11 May 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 24 May 2012 VIIRS-SDR-RSR-LUT; 08 June 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 09 July 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 03 August 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-SDR-DNB-C-COEFFS-LUT; 09 August 2012 VIIRS-SDR-QA-LUT; VIIRS-SDR-RSR-LUT; 10 August 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 17 August 2012 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 06 September 2012 VIIRS-SDR-RSR-LUT; 27 September 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 31 October 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 29 November 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-SDR-GEO-DNB-PARAM-LUT; 11 December 2012 VIIRS-SDR-GEO-IMG-PARAM-LUT; VIIRS-SDR-GEO-MOD-PARAM-LUT; 20 December 2012 VIIRS-SDR-DNB-C-COEFFS-LUT; 24 January 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-SDR-DNB-C-COEFFS-LUT; 14 February 2013 VIIRS-SDR-GEO-DNB-PARAM-LUT; 21 February 2013 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 21 March 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; 28 March 2013 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 05 April 2013 VIIRS-SDR-RSR-LUT; VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-SDR-GEO-DNB-PARAM-LUT; 18 April 2013 VIIRS-SDR-GEO-IMG-PARAM-LUT; VIIRS-SDR-GEO-MOD-PARAM-LUT; VIIRS-SDR-GEO-MOD-PARAM-LUT; Remote Sens. 2021, 13, 1075 32 of 35

16 May 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; 20 June 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; 10 July 2013 VIIRS-SDR-RADIOMETRIC-PARAM-LUT; 18 July 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; 08 August 2013 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 19 August 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-SDR-GEO-DNB-PARAM-LUT; 23 August 2013 VIIRS-SDR-GEO-IMG-PARAM-LUT; VIIRS-SDR-GEO-MOD-PARAM-LUT; 19 September 2013 VIIRS-SDR-DNB-C-COEFFS-LUT; VIIRS-RSBAUTOCAL-VOLT-LUT; VIIRS-SDR-CAL-AUTOMATE-LUT; 14 November 2013 VIIRS-SDR-RADIOMETRIC-PARAM-V2-LUT; VIIRS-SDR-RELATIVE-SPECTRAL- RESPONSE-LUT; VIIRS-SDR-RELATIVE-SPECTRAL- RESPONSE-LUT; VIIRS-SDR-RADIOMETRIC-PARAM-V3- 18 March 2014 LUT; 10 April 2014 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 01 May 2014 VIIRS-SDR-DELTA-C-LUT; 22 May 2014 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; 25 September 2014 VIIRS-SDR-DG-ANOMALY-DN-LIMITS-LUT; VIIRS-SDR-RELATIVE-SPECTRAL- 01 October 2014 RESPONSE-LUT; 06 March 2015 VIIRS-SDR-DELTA-C-LUT; 22 June 2015 VIIRS-SDR-EBBT-LUT; 30 November 2015 VIIRS-SDR-CAL-AUTOMATE-LUT; 05 January 2017 DNB-DN0 CMNGEO-PARAM-LUT; 08 March 2017 VIIRS-RSBAUTOCAL-VOLT-LUT; VIIRS-SDR-BB-TEMP-COEFFS-LUT; VIIRS-SDR-CAL-AUTOMATE-LUT; VIIRS-SDR-COEFF-A-LUT; VIIRS-SDR-COEFF-B-LUT; VIIRS-SDR-DELTA-C-LUT; VIIRS-SDR-DNB-FRAME-TO-ZONE-LUT; VIIRS-SDR-DNB-RVF-LUT; VIIRS-SDR-EBBT-LUT; VIIRS-SDR-EMISSIVE-LUT; VIIRS-SDR-GAIN-LUT; VIIRS-SDR-HAM-ER-LUT; VIIRS-SDR-OBC-ER-LUT; VIIRS-SDR-OBC-RR-LUT; VIIRS-SDR-OBS-TO-PIXELS-LUT; VIIRS-SDR-QA-LUT; VIIRS-SDR-RADIOMETRIC-PARAM-V3-LUT; Remote Sens. 2021, 13, 1075 33 of 35

VIIRS-SDR-REFLECTIVE-LUT; VIIRS-SDR- RELATIVE-SPECTRAL-RESPONSE-LUT; VIIRS-SDR-RTA-ER-LUT; VIIRS-SDR-RVF-LUT; VIIRS-SDR-SOLAR-IRAD-LUT; VIIRS-SDR-TELE-COEFFS-LUT;

In addition, RSBAUTOCAL_HISTORY_AUX LUT was updated daily from 15 Novem- ber 2013; before that time, the F-PREDICT LUT, updated daily, was used for RSB F factor. For DNB, there were also periodical updates for DN0, LGS-GAIN and GAIN-RATIO LUTs. These periodically updated LUTs are not shown in the Tables above. During reprocessing, these LUTs were also different from operational ones.

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